{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 人脸识别与计算机视觉文档实践\n",
    "\n",
    ">* 本周主要内容：人脸（Face） API文档不同平台对比与实践及计算机视觉入门（认知服务）\n",
    ">* 202009_API_人工智能与机器学习_week03\n",
    ">*  电子讲义设计者：许智超\n",
    "<br/>\n",
    "<br/>\n",
    "\n",
    "## 复习\n",
    "\n",
    "* 上周主要内容： \n",
    ">    * 1、API文档\n",
    ">    * 2、认知服务-人脸识别\n",
    ">    * 3、pandas黑魔法（json_normalize）\n",
    "-----\n",
    "* 提问、检查与扩展：\n",
    ">    * 1、上周的Azure face API随机检查2位同学，对于API文档是否有能力详细阅读？\n",
    ">    * 2、抽查2位同学，对于获取的数据用途有何思考？是否尝试其他平台（XXX API、XXX API）      \n",
    ">    * 3、扩展: 我们尝试抽取了4张图片，包含AB、ABC、BCD、ABCD四个人物特征，如何通过API检查数据差异？\n",
    "  \n",
    "\n",
    "-----\n",
    "## 本周学习目标：\n",
    "\n",
    "> $\\mathcal{1、Azure 认知服务-人脸集合演示}$     \n",
    "> $\\mathcal{2、Face++ 之 FaceSets 实践}$     \n",
    "> $\\mathcal{3、计算机视觉实践}$    \n",
    "\n",
    "\n",
    "\n",
    "## 学生权限\n",
    "\n",
    "* [访问学生权益](https://azure.microsoft.com/zh-cn/education/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "/* 本电子讲义使用之CSS */\n",
       "div.code_cell {\n",
       "    background-color: #e5f1fe;\n",
       "}\n",
       "div.cell.selected {\n",
       "    background-color: #effee2;\n",
       "    font-size: 2rem;\n",
       "    line-height: 2.4rem;\n",
       "}\n",
       "div.cell.selected .rendered_html table {\n",
       "    font-size: 2rem !important;\n",
       "    line-height: 2.4rem !important;\n",
       "}\n",
       ".rendered_html pre code {\n",
       "    background-color: #C4E4ff;   \n",
       "    padding: 2px 25px;\n",
       "}\n",
       ".rendered_html pre {\n",
       "    background-color: #99c9ff;\n",
       "}\n",
       "div.code_cell .CodeMirror {\n",
       "    font-size: 2rem !important;\n",
       "    line-height: 2.4rem !important;\n",
       "}\n",
       ".rendered_html img, .rendered_html svg {\n",
       "    max-width: 50%;\n",
       "    height: auto;\n",
       "    float: center;\n",
       "}\n",
       "/* Gradient transparent - color - transparent */\n",
       "hr {\n",
       "    border: 0;\n",
       "    border-bottom: 1px dashed #ccc;\n",
       "}\n",
       ".emoticon{\n",
       "    font-size: 5rem;\n",
       "    line-height: 4.4rem;\n",
       "    text-align: center;\n",
       "    vertical-align: middle;\n",
       "}\n",
       "\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<style>\n",
    "/* 本电子讲义使用之CSS */\n",
    "div.code_cell {\n",
    "    background-color: #e5f1fe;\n",
    "}\n",
    "div.cell.selected {\n",
    "    background-color: #effee2;\n",
    "    font-size: 2rem;\n",
    "    line-height: 2.4rem;\n",
    "}\n",
    "div.cell.selected .rendered_html table {\n",
    "    font-size: 2rem !important;\n",
    "    line-height: 2.4rem !important;\n",
    "}\n",
    ".rendered_html pre code {\n",
    "    background-color: #C4E4ff;   \n",
    "    padding: 2px 25px;\n",
    "}\n",
    ".rendered_html pre {\n",
    "    background-color: #99c9ff;\n",
    "}\n",
    "div.code_cell .CodeMirror {\n",
    "    font-size: 2rem !important;\n",
    "    line-height: 2.4rem !important;\n",
    "}\n",
    ".rendered_html img, .rendered_html svg {\n",
    "    max-width: 50%;\n",
    "    height: auto;\n",
    "    float: center;\n",
    "}\n",
    "/* Gradient transparent - color - transparent */\n",
    "hr {\n",
    "    border: 0;\n",
    "    border-bottom: 1px dashed #ccc;\n",
    "}\n",
    ".emoticon{\n",
    "    font-size: 5rem;\n",
    "    line-height: 4.4rem;\n",
    "    text-align: center;\n",
    "    vertical-align: middle;\n",
    "}\n",
    "\n",
    "</style>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 复习\n",
    "\n",
    "> 1、回顾阅读API文档的关键点     \n",
    "> 2、正确阅读json数据 [jsonviewer.stack.hu(json转换检查数据)](http://jsonviewer.stack.hu)    \n",
    "> 3、pandas 中的json_normalize模块/函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-1 面部检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"faceId\": \"00011785-70b5-4325-be59-624722fd4f13\", \"faceRectangle\": {\"top\": 118, \"left\": 144, \"width\": 88, \"height\": 88}, \"faceAttributes\": {\"smile\": 0.813, \"headPose\": {\"pitch\": -3.4, \"roll\": 1.4, \"yaw\": 9.4}, \"gender\": \"male\", \"age\": 19.0, \"facialHair\": {\"moustache\": 0.1, \"beard\": 0.1, \"sideburns\": 0.1}, \"glasses\": \"NoGlasses\", \"emotion\": {\"anger\": 0.0, \"contempt\": 0.003, \"disgust\": 0.0, \"fear\": 0.0, \"happiness\": 0.813, \"neutral\": 0.184, \"sadness\": 0.0, \"surprise\": 0.0}, \"blur\": {\"blurLevel\": \"low\", \"value\": 0.21}, \"exposure\": {\"exposureLevel\": \"overExposure\", \"value\": 0.81}, \"noise\": {\"noiseLevel\": \"low\", \"value\": 0.01}, \"makeup\": {\"eyeMakeup\": false, \"lipMakeup\": false}, \"accessories\": [], \"occlusion\": {\"foreheadOccluded\": false, \"eyeOccluded\": false, \"mouthOccluded\": false}, \"hair\": {\"bald\": 0.18, \"invisible\": false, \"hairColor\": [{\"color\": \"brown\", \"confidence\": 0.95}, {\"color\": \"black\", \"confidence\": 0.93}, {\"color\": \"other\", \"confidence\": 0.23}, {\"color\": \"blond\", \"confidence\": 0.23}, {\"color\": \"gray\", \"confidence\": 0.21}, {\"color\": \"red\", \"confidence\": 0.15}, {\"color\": \"white\", \"confidence\": 0.0}]}}}, {\"faceId\": \"1a148e4d-ece7-4019-a4b7-d098ad66be0b\", \"faceRectangle\": {\"top\": 117, \"left\": 376, \"width\": 64, \"height\": 64}, \"faceAttributes\": {\"smile\": 0.456, \"headPose\": {\"pitch\": -1.1, \"roll\": -0.2, \"yaw\": 6.6}, \"gender\": \"female\", \"age\": 22.0, \"facialHair\": {\"moustache\": 0.0, \"beard\": 0.0, \"sideburns\": 0.0}, \"glasses\": \"NoGlasses\", \"emotion\": {\"anger\": 0.0, \"contempt\": 0.001, \"disgust\": 0.0, \"fear\": 0.0, \"happiness\": 0.456, \"neutral\": 0.542, \"sadness\": 0.001, \"surprise\": 0.0}, \"blur\": {\"blurLevel\": \"low\", \"value\": 0.16}, \"exposure\": {\"exposureLevel\": \"goodExposure\", \"value\": 0.58}, \"noise\": {\"noiseLevel\": \"low\", \"value\": 0.01}, \"makeup\": {\"eyeMakeup\": false, \"lipMakeup\": true}, \"accessories\": [], \"occlusion\": {\"foreheadOccluded\": false, \"eyeOccluded\": false, \"mouthOccluded\": false}, \"hair\": {\"bald\": 0.08, \"invisible\": false, \"hairColor\": [{\"color\": \"black\", \"confidence\": 1.0}, {\"color\": \"other\", \"confidence\": 0.63}, {\"color\": \"brown\", \"confidence\": 0.46}, {\"color\": \"gray\", \"confidence\": 0.39}, {\"color\": \"blond\", \"confidence\": 0.03}, {\"color\": \"red\", \"confidence\": 0.01}, {\"color\": \"white\", \"confidence\": 0.0}]}}}, {\"faceId\": \"986ab02c-00a2-4c99-80ba-1c1a0025db45\", \"faceRectangle\": {\"top\": 41, \"left\": 676, \"width\": 52, \"height\": 52}, \"faceAttributes\": {\"smile\": 1.0, \"headPose\": {\"pitch\": 3.0, \"roll\": -1.3, \"yaw\": -0.6}, \"gender\": \"male\", \"age\": 25.0, \"facialHair\": {\"moustache\": 0.1, \"beard\": 0.1, \"sideburns\": 0.1}, \"glasses\": \"ReadingGlasses\", \"emotion\": {\"anger\": 0.0, \"contempt\": 0.0, \"disgust\": 0.0, \"fear\": 0.0, \"happiness\": 1.0, \"neutral\": 0.0, \"sadness\": 0.0, \"surprise\": 0.0}, \"blur\": {\"blurLevel\": \"low\", \"value\": 0.0}, \"exposure\": {\"exposureLevel\": \"goodExposure\", \"value\": 0.66}, \"noise\": {\"noiseLevel\": \"low\", \"value\": 0.07}, \"makeup\": {\"eyeMakeup\": false, \"lipMakeup\": false}, \"accessories\": [{\"type\": \"glasses\", \"confidence\": 1.0}], \"occlusion\": {\"foreheadOccluded\": false, \"eyeOccluded\": false, \"mouthOccluded\": false}, \"hair\": {\"bald\": 0.06, \"invisible\": false, \"hairColor\": [{\"color\": \"black\", \"confidence\": 0.98}, {\"color\": \"brown\", \"confidence\": 0.96}, {\"color\": \"other\", \"confidence\": 0.22}, {\"color\": \"gray\", \"confidence\": 0.21}, {\"color\": \"red\", \"confidence\": 0.09}, {\"color\": \"blond\", \"confidence\": 0.08}, {\"color\": \"white\", \"confidence\": 0.0}]}}}, {\"faceId\": \"dd6a85d9-23a4-4486-9f65-9fb077ff6037\", \"faceRectangle\": {\"top\": 69, \"left\": 445, \"width\": 52, \"height\": 52}, \"faceAttributes\": {\"smile\": 1.0, \"headPose\": {\"pitch\": 0.2, \"roll\": 2.6, \"yaw\": 1.5}, \"gender\": \"female\", \"age\": 23.0, \"facialHair\": {\"moustache\": 0.0, \"beard\": 0.0, \"sideburns\": 0.0}, \"glasses\": \"NoGlasses\", \"emotion\": {\"anger\": 0.0, \"contempt\": 0.0, \"disgust\": 0.0, \"fear\": 0.0, \"happiness\": 1.0, \"neutral\": 0.0, \"sadness\": 0.0, \"surprise\": 0.0}, \"blur\": {\"blurLevel\": \"low\", \"value\": 0.0}, \"exposure\": {\"exposureLevel\": \"goodExposure\", \"value\": 0.56}, \"noise\": {\"noiseLevel\": \"medium\", \"value\": 0.33}, \"makeup\": {\"eyeMakeup\": false, \"lipMakeup\": false}, \"accessories\": [], \"occlusion\": {\"foreheadOccluded\": false, \"eyeOccluded\": false, \"mouthOccluded\": false}, \"hair\": {\"bald\": 0.11, \"invisible\": false, \"hairColor\": [{\"color\": \"black\", \"confidence\": 1.0}, {\"color\": \"brown\", \"confidence\": 0.81}, {\"color\": \"other\", \"confidence\": 0.41}, {\"color\": \"gray\", \"confidence\": 0.37}, {\"color\": \"blond\", \"confidence\": 0.03}, {\"color\": \"red\", \"confidence\": 0.02}, {\"color\": \"white\", \"confidence\": 0.0}]}}}, {\"faceId\": \"85d03d31-93b9-4e85-ac91-3d3f945558c5\", \"faceRectangle\": {\"top\": 95, \"left\": 238, \"width\": 51, \"height\": 51}, \"faceAttributes\": {\"smile\": 0.981, \"headPose\": {\"pitch\": 7.2, \"roll\": 1.2, \"yaw\": 2.6}, \"gender\": \"female\", \"age\": 18.0, \"facialHair\": {\"moustache\": 0.0, \"beard\": 0.0, \"sideburns\": 0.0}, \"glasses\": \"ReadingGlasses\", \"emotion\": {\"anger\": 0.0, \"contempt\": 0.0, \"disgust\": 0.0, \"fear\": 0.0, \"happiness\": 0.981, \"neutral\": 0.019, \"sadness\": 0.0, \"surprise\": 0.0}, \"blur\": {\"blurLevel\": \"low\", \"value\": 0.07}, \"exposure\": {\"exposureLevel\": \"goodExposure\", \"value\": 0.69}, \"noise\": {\"noiseLevel\": \"medium\", \"value\": 0.48}, \"makeup\": {\"eyeMakeup\": false, \"lipMakeup\": false}, \"accessories\": [{\"type\": \"glasses\", \"confidence\": 1.0}], \"occlusion\": {\"foreheadOccluded\": false, \"eyeOccluded\": false, \"mouthOccluded\": false}, \"hair\": {\"bald\": 0.11, \"invisible\": false, \"hairColor\": [{\"color\": \"black\", \"confidence\": 0.98}, {\"color\": \"brown\", \"confidence\": 0.86}, {\"color\": \"other\", \"confidence\": 0.4}, {\"color\": \"gray\", \"confidence\": 0.36}, {\"color\": \"blond\", \"confidence\": 0.09}, {\"color\": \"red\", \"confidence\": 0.08}, {\"color\": \"white\", \"confidence\": 0.0}]}}}, {\"faceId\": \"278e1222-dd78-4610-b5e1-a2afb2ff0d84\", \"faceRectangle\": {\"top\": 94, \"left\": 540, \"width\": 48, \"height\": 48}, \"faceAttributes\": {\"smile\": 1.0, \"headPose\": {\"pitch\": -7.7, \"roll\": 5.0, \"yaw\": 6.9}, \"gender\": \"female\", \"age\": 19.0, \"facialHair\": {\"moustache\": 0.0, \"beard\": 0.0, \"sideburns\": 0.0}, \"glasses\": \"NoGlasses\", \"emotion\": {\"anger\": 0.0, \"contempt\": 0.0, \"disgust\": 0.0, \"fear\": 0.0, \"happiness\": 1.0, \"neutral\": 0.0, \"sadness\": 0.0, \"surprise\": 0.0}, \"blur\": {\"blurLevel\": \"low\", \"value\": 0.08}, \"exposure\": {\"exposureLevel\": \"goodExposure\", \"value\": 0.65}, \"noise\": {\"noiseLevel\": \"low\", \"value\": 0.08}, \"makeup\": {\"eyeMakeup\": false, \"lipMakeup\": false}, \"accessories\": [], \"occlusion\": {\"foreheadOccluded\": false, \"eyeOccluded\": false, \"mouthOccluded\": false}, \"hair\": {\"bald\": 0.1, \"invisible\": false, \"hairColor\": [{\"color\": \"black\", \"confidence\": 1.0}, {\"color\": \"brown\", \"confidence\": 0.88}, {\"color\": \"other\", \"confidence\": 0.35}, {\"color\": \"gray\", \"confidence\": 0.33}, {\"color\": \"blond\", \"confidence\": 0.05}, {\"color\": \"red\", \"confidence\": 0.04}, {\"color\": \"white\", \"confidence\": 0.0}]}}}]'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-1 面部检测\n",
    "import requests\n",
    "import json\n",
    "\n",
    "# set to your own subscription key value\n",
    "subscription_key = \"aa04a3698a2c413b9fce981fdca00510\"\n",
    "assert subscription_key\n",
    "\n",
    "# replace <My Endpoint String> with the string from your endpoint URL\n",
    "face_api_url = 'https://api-xyr.cognitiveservices.azure.com/face/v1.0/detect'\n",
    "\n",
    "# 请求正文body\n",
    "image_url = 'http://newmedia.nfu.edu.cn/wcy/wp-content/uploads/2018/04/post_20180424__NFU_DoraHacks_imoji%E5%9B%A2%E9%98%9F.jpg'\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "\n",
    "# 请求参数parameters\n",
    "params = {\n",
    "    'returnFaceId': 'true',\n",
    "    'returnFaceLandmarks': 'false',\n",
    "    # 可选参数,请仔细阅读API文档\n",
    "    'returnFaceAttributes': 'age,gender,headPose,smile,facialHair,glasses,emotion,hair,makeup,occlusion,accessories,blur,exposure,noise',\n",
    "}\n",
    "\n",
    "response = requests.post(face_api_url, params=params,\n",
    "                         headers=headers, json={\"url\": image_url})\n",
    "# json.dumps 将json--->bytes\n",
    "json.dumps(response.json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-2 json转译\n",
    "\n",
    "> * bytes ---> json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'faceId': '00011785-70b5-4325-be59-624722fd4f13',\n",
       "  'faceRectangle': {'top': 118, 'left': 144, 'width': 88, 'height': 88},\n",
       "  'faceAttributes': {'smile': 0.813,\n",
       "   'headPose': {'pitch': -3.4, 'roll': 1.4, 'yaw': 9.4},\n",
       "   'gender': 'male',\n",
       "   'age': 19.0,\n",
       "   'facialHair': {'moustache': 0.1, 'beard': 0.1, 'sideburns': 0.1},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.003,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 0.813,\n",
       "    'neutral': 0.184,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'low', 'value': 0.21},\n",
       "   'exposure': {'exposureLevel': 'overExposure', 'value': 0.81},\n",
       "   'noise': {'noiseLevel': 'low', 'value': 0.01},\n",
       "   'makeup': {'eyeMakeup': False, 'lipMakeup': False},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.18,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'brown', 'confidence': 0.95},\n",
       "     {'color': 'black', 'confidence': 0.93},\n",
       "     {'color': 'other', 'confidence': 0.23},\n",
       "     {'color': 'blond', 'confidence': 0.23},\n",
       "     {'color': 'gray', 'confidence': 0.21},\n",
       "     {'color': 'red', 'confidence': 0.15},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}},\n",
       " {'faceId': '1a148e4d-ece7-4019-a4b7-d098ad66be0b',\n",
       "  'faceRectangle': {'top': 117, 'left': 376, 'width': 64, 'height': 64},\n",
       "  'faceAttributes': {'smile': 0.456,\n",
       "   'headPose': {'pitch': -1.1, 'roll': -0.2, 'yaw': 6.6},\n",
       "   'gender': 'female',\n",
       "   'age': 22.0,\n",
       "   'facialHair': {'moustache': 0.0, 'beard': 0.0, 'sideburns': 0.0},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.001,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 0.456,\n",
       "    'neutral': 0.542,\n",
       "    'sadness': 0.001,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'low', 'value': 0.16},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.58},\n",
       "   'noise': {'noiseLevel': 'low', 'value': 0.01},\n",
       "   'makeup': {'eyeMakeup': False, 'lipMakeup': True},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.08,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'black', 'confidence': 1.0},\n",
       "     {'color': 'other', 'confidence': 0.63},\n",
       "     {'color': 'brown', 'confidence': 0.46},\n",
       "     {'color': 'gray', 'confidence': 0.39},\n",
       "     {'color': 'blond', 'confidence': 0.03},\n",
       "     {'color': 'red', 'confidence': 0.01},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}},\n",
       " {'faceId': '986ab02c-00a2-4c99-80ba-1c1a0025db45',\n",
       "  'faceRectangle': {'top': 41, 'left': 676, 'width': 52, 'height': 52},\n",
       "  'faceAttributes': {'smile': 1.0,\n",
       "   'headPose': {'pitch': 3.0, 'roll': -1.3, 'yaw': -0.6},\n",
       "   'gender': 'male',\n",
       "   'age': 25.0,\n",
       "   'facialHair': {'moustache': 0.1, 'beard': 0.1, 'sideburns': 0.1},\n",
       "   'glasses': 'ReadingGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.0,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 1.0,\n",
       "    'neutral': 0.0,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'low', 'value': 0.0},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.66},\n",
       "   'noise': {'noiseLevel': 'low', 'value': 0.07},\n",
       "   'makeup': {'eyeMakeup': False, 'lipMakeup': False},\n",
       "   'accessories': [{'type': 'glasses', 'confidence': 1.0}],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.06,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'black', 'confidence': 0.98},\n",
       "     {'color': 'brown', 'confidence': 0.96},\n",
       "     {'color': 'other', 'confidence': 0.22},\n",
       "     {'color': 'gray', 'confidence': 0.21},\n",
       "     {'color': 'red', 'confidence': 0.09},\n",
       "     {'color': 'blond', 'confidence': 0.08},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}},\n",
       " {'faceId': 'dd6a85d9-23a4-4486-9f65-9fb077ff6037',\n",
       "  'faceRectangle': {'top': 69, 'left': 445, 'width': 52, 'height': 52},\n",
       "  'faceAttributes': {'smile': 1.0,\n",
       "   'headPose': {'pitch': 0.2, 'roll': 2.6, 'yaw': 1.5},\n",
       "   'gender': 'female',\n",
       "   'age': 23.0,\n",
       "   'facialHair': {'moustache': 0.0, 'beard': 0.0, 'sideburns': 0.0},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.0,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 1.0,\n",
       "    'neutral': 0.0,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'low', 'value': 0.0},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.56},\n",
       "   'noise': {'noiseLevel': 'medium', 'value': 0.33},\n",
       "   'makeup': {'eyeMakeup': False, 'lipMakeup': False},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.11,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'black', 'confidence': 1.0},\n",
       "     {'color': 'brown', 'confidence': 0.81},\n",
       "     {'color': 'other', 'confidence': 0.41},\n",
       "     {'color': 'gray', 'confidence': 0.37},\n",
       "     {'color': 'blond', 'confidence': 0.03},\n",
       "     {'color': 'red', 'confidence': 0.02},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}},\n",
       " {'faceId': '85d03d31-93b9-4e85-ac91-3d3f945558c5',\n",
       "  'faceRectangle': {'top': 95, 'left': 238, 'width': 51, 'height': 51},\n",
       "  'faceAttributes': {'smile': 0.981,\n",
       "   'headPose': {'pitch': 7.2, 'roll': 1.2, 'yaw': 2.6},\n",
       "   'gender': 'female',\n",
       "   'age': 18.0,\n",
       "   'facialHair': {'moustache': 0.0, 'beard': 0.0, 'sideburns': 0.0},\n",
       "   'glasses': 'ReadingGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.0,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 0.981,\n",
       "    'neutral': 0.019,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'low', 'value': 0.07},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.69},\n",
       "   'noise': {'noiseLevel': 'medium', 'value': 0.48},\n",
       "   'makeup': {'eyeMakeup': False, 'lipMakeup': False},\n",
       "   'accessories': [{'type': 'glasses', 'confidence': 1.0}],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.11,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'black', 'confidence': 0.98},\n",
       "     {'color': 'brown', 'confidence': 0.86},\n",
       "     {'color': 'other', 'confidence': 0.4},\n",
       "     {'color': 'gray', 'confidence': 0.36},\n",
       "     {'color': 'blond', 'confidence': 0.09},\n",
       "     {'color': 'red', 'confidence': 0.08},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}},\n",
       " {'faceId': '278e1222-dd78-4610-b5e1-a2afb2ff0d84',\n",
       "  'faceRectangle': {'top': 94, 'left': 540, 'width': 48, 'height': 48},\n",
       "  'faceAttributes': {'smile': 1.0,\n",
       "   'headPose': {'pitch': -7.7, 'roll': 5.0, 'yaw': 6.9},\n",
       "   'gender': 'female',\n",
       "   'age': 19.0,\n",
       "   'facialHair': {'moustache': 0.0, 'beard': 0.0, 'sideburns': 0.0},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.0,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 1.0,\n",
       "    'neutral': 0.0,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'low', 'value': 0.08},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.65},\n",
       "   'noise': {'noiseLevel': 'low', 'value': 0.08},\n",
       "   'makeup': {'eyeMakeup': False, 'lipMakeup': False},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.1,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'black', 'confidence': 1.0},\n",
       "     {'color': 'brown', 'confidence': 0.88},\n",
       "     {'color': 'other', 'confidence': 0.35},\n",
       "     {'color': 'gray', 'confidence': 0.33},\n",
       "     {'color': 'blond', 'confidence': 0.05},\n",
       "     {'color': 'red', 'confidence': 0.04},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}}]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-2\n",
    "results = response.json()\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-3 pandas 数据表格化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>faceId</th>\n",
       "      <th>faceRectangle.top</th>\n",
       "      <th>faceRectangle.left</th>\n",
       "      <th>faceRectangle.width</th>\n",
       "      <th>faceRectangle.height</th>\n",
       "      <th>faceAttributes.smile</th>\n",
       "      <th>faceAttributes.headPose.pitch</th>\n",
       "      <th>faceAttributes.headPose.roll</th>\n",
       "      <th>faceAttributes.headPose.yaw</th>\n",
       "      <th>faceAttributes.gender</th>\n",
       "      <th>...</th>\n",
       "      <th>faceAttributes.noise.value</th>\n",
       "      <th>faceAttributes.makeup.eyeMakeup</th>\n",
       "      <th>faceAttributes.makeup.lipMakeup</th>\n",
       "      <th>faceAttributes.accessories</th>\n",
       "      <th>faceAttributes.occlusion.foreheadOccluded</th>\n",
       "      <th>faceAttributes.occlusion.eyeOccluded</th>\n",
       "      <th>faceAttributes.occlusion.mouthOccluded</th>\n",
       "      <th>faceAttributes.hair.bald</th>\n",
       "      <th>faceAttributes.hair.invisible</th>\n",
       "      <th>faceAttributes.hair.hairColor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00011785-70b5-4325-be59-624722fd4f13</td>\n",
       "      <td>118</td>\n",
       "      <td>144</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>0.813</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>male</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.18</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'brown', 'confidence': 0.95}, {'col...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1a148e4d-ece7-4019-a4b7-d098ad66be0b</td>\n",
       "      <td>117</td>\n",
       "      <td>376</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>0.456</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>6.6</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.08</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 1.0}, {'colo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>986ab02c-00a2-4c99-80ba-1c1a0025db45</td>\n",
       "      <td>41</td>\n",
       "      <td>676</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>male</td>\n",
       "      <td>...</td>\n",
       "      <td>0.07</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'type': 'glasses', 'confidence': 1.0}]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.06</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 0.98}, {'col...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dd6a85d9-23a4-4486-9f65-9fb077ff6037</td>\n",
       "      <td>69</td>\n",
       "      <td>445</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>0.33</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.11</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 1.0}, {'colo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85d03d31-93b9-4e85-ac91-3d3f945558c5</td>\n",
       "      <td>95</td>\n",
       "      <td>238</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>0.981</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>0.48</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'type': 'glasses', 'confidence': 1.0}]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.11</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 0.98}, {'col...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>278e1222-dd78-4610-b5e1-a2afb2ff0d84</td>\n",
       "      <td>94</td>\n",
       "      <td>540</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.10</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 1.0}, {'colo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 faceId  faceRectangle.top  \\\n",
       "0  00011785-70b5-4325-be59-624722fd4f13                118   \n",
       "1  1a148e4d-ece7-4019-a4b7-d098ad66be0b                117   \n",
       "2  986ab02c-00a2-4c99-80ba-1c1a0025db45                 41   \n",
       "3  dd6a85d9-23a4-4486-9f65-9fb077ff6037                 69   \n",
       "4  85d03d31-93b9-4e85-ac91-3d3f945558c5                 95   \n",
       "5  278e1222-dd78-4610-b5e1-a2afb2ff0d84                 94   \n",
       "\n",
       "   faceRectangle.left  faceRectangle.width  faceRectangle.height  \\\n",
       "0                 144                   88                    88   \n",
       "1                 376                   64                    64   \n",
       "2                 676                   52                    52   \n",
       "3                 445                   52                    52   \n",
       "4                 238                   51                    51   \n",
       "5                 540                   48                    48   \n",
       "\n",
       "   faceAttributes.smile  faceAttributes.headPose.pitch  \\\n",
       "0                 0.813                           -3.4   \n",
       "1                 0.456                           -1.1   \n",
       "2                 1.000                            3.0   \n",
       "3                 1.000                            0.2   \n",
       "4                 0.981                            7.2   \n",
       "5                 1.000                           -7.7   \n",
       "\n",
       "   faceAttributes.headPose.roll  faceAttributes.headPose.yaw  \\\n",
       "0                           1.4                          9.4   \n",
       "1                          -0.2                          6.6   \n",
       "2                          -1.3                         -0.6   \n",
       "3                           2.6                          1.5   \n",
       "4                           1.2                          2.6   \n",
       "5                           5.0                          6.9   \n",
       "\n",
       "  faceAttributes.gender  ...  faceAttributes.noise.value  \\\n",
       "0                  male  ...                        0.01   \n",
       "1                female  ...                        0.01   \n",
       "2                  male  ...                        0.07   \n",
       "3                female  ...                        0.33   \n",
       "4                female  ...                        0.48   \n",
       "5                female  ...                        0.08   \n",
       "\n",
       "   faceAttributes.makeup.eyeMakeup  faceAttributes.makeup.lipMakeup  \\\n",
       "0                            False                            False   \n",
       "1                            False                             True   \n",
       "2                            False                            False   \n",
       "3                            False                            False   \n",
       "4                            False                            False   \n",
       "5                            False                            False   \n",
       "\n",
       "                 faceAttributes.accessories  \\\n",
       "0                                        []   \n",
       "1                                        []   \n",
       "2  [{'type': 'glasses', 'confidence': 1.0}]   \n",
       "3                                        []   \n",
       "4  [{'type': 'glasses', 'confidence': 1.0}]   \n",
       "5                                        []   \n",
       "\n",
       "  faceAttributes.occlusion.foreheadOccluded  \\\n",
       "0                                     False   \n",
       "1                                     False   \n",
       "2                                     False   \n",
       "3                                     False   \n",
       "4                                     False   \n",
       "5                                     False   \n",
       "\n",
       "   faceAttributes.occlusion.eyeOccluded  \\\n",
       "0                                 False   \n",
       "1                                 False   \n",
       "2                                 False   \n",
       "3                                 False   \n",
       "4                                 False   \n",
       "5                                 False   \n",
       "\n",
       "   faceAttributes.occlusion.mouthOccluded  faceAttributes.hair.bald  \\\n",
       "0                                   False                      0.18   \n",
       "1                                   False                      0.08   \n",
       "2                                   False                      0.06   \n",
       "3                                   False                      0.11   \n",
       "4                                   False                      0.11   \n",
       "5                                   False                      0.10   \n",
       "\n",
       "   faceAttributes.hair.invisible  \\\n",
       "0                          False   \n",
       "1                          False   \n",
       "2                          False   \n",
       "3                          False   \n",
       "4                          False   \n",
       "5                          False   \n",
       "\n",
       "                       faceAttributes.hair.hairColor  \n",
       "0  [{'color': 'brown', 'confidence': 0.95}, {'col...  \n",
       "1  [{'color': 'black', 'confidence': 1.0}, {'colo...  \n",
       "2  [{'color': 'black', 'confidence': 0.98}, {'col...  \n",
       "3  [{'color': 'black', 'confidence': 1.0}, {'colo...  \n",
       "4  [{'color': 'black', 'confidence': 0.98}, {'col...  \n",
       "5  [{'color': 'black', 'confidence': 1.0}, {'colo...  \n",
       "\n",
       "[6 rows x 38 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-3\n",
    "import pandas as pd\n",
    "df_face = pd.json_normalize(results)\n",
    "df_face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-4 数据取值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['00011785-70b5-4325-be59-624722fd4f13',\n",
       " '1a148e4d-ece7-4019-a4b7-d098ad66be0b',\n",
       " '986ab02c-00a2-4c99-80ba-1c1a0025db45',\n",
       " 'dd6a85d9-23a4-4486-9f65-9fb077ff6037',\n",
       " '85d03d31-93b9-4e85-ac91-3d3f945558c5',\n",
       " '278e1222-dd78-4610-b5e1-a2afb2ff0d84']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faceID = df_face['faceId'].values.tolist()\n",
    "faceID "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['faceId', 'faceRectangle.top', 'faceRectangle.left',\n",
       "       'faceRectangle.width', 'faceRectangle.height', 'faceAttributes.smile',\n",
       "       'faceAttributes.headPose.pitch', 'faceAttributes.headPose.roll',\n",
       "       'faceAttributes.headPose.yaw', 'faceAttributes.gender',\n",
       "       'faceAttributes.age', 'faceAttributes.facialHair.moustache',\n",
       "       'faceAttributes.facialHair.beard',\n",
       "       'faceAttributes.facialHair.sideburns', 'faceAttributes.glasses',\n",
       "       'faceAttributes.emotion.anger', 'faceAttributes.emotion.contempt',\n",
       "       'faceAttributes.emotion.disgust', 'faceAttributes.emotion.fear',\n",
       "       'faceAttributes.emotion.happiness', 'faceAttributes.emotion.neutral',\n",
       "       'faceAttributes.emotion.sadness', 'faceAttributes.emotion.surprise',\n",
       "       'faceAttributes.blur.blurLevel', 'faceAttributes.blur.value',\n",
       "       'faceAttributes.exposure.exposureLevel',\n",
       "       'faceAttributes.exposure.value', 'faceAttributes.noise.noiseLevel',\n",
       "       'faceAttributes.noise.value', 'faceAttributes.makeup.eyeMakeup',\n",
       "       'faceAttributes.makeup.lipMakeup', 'faceAttributes.accessories',\n",
       "       'faceAttributes.occlusion.foreheadOccluded',\n",
       "       'faceAttributes.occlusion.eyeOccluded',\n",
       "       'faceAttributes.occlusion.mouthOccluded', 'faceAttributes.hair.bald',\n",
       "       'faceAttributes.hair.invisible', 'faceAttributes.hair.hairColor'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查属性/特征值\n",
    "df_face.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>faceId</th>\n",
       "      <th>faceAttributes.glasses</th>\n",
       "      <th>faceAttributes.emotion.neutral</th>\n",
       "      <th>faceAttributes.age</th>\n",
       "      <th>faceAttributes.gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00011785-70b5-4325-be59-624722fd4f13</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.184</td>\n",
       "      <td>19.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1a148e4d-ece7-4019-a4b7-d098ad66be0b</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.542</td>\n",
       "      <td>22.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>986ab02c-00a2-4c99-80ba-1c1a0025db45</td>\n",
       "      <td>ReadingGlasses</td>\n",
       "      <td>0.000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dd6a85d9-23a4-4486-9f65-9fb077ff6037</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85d03d31-93b9-4e85-ac91-3d3f945558c5</td>\n",
       "      <td>ReadingGlasses</td>\n",
       "      <td>0.019</td>\n",
       "      <td>18.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>278e1222-dd78-4610-b5e1-a2afb2ff0d84</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.000</td>\n",
       "      <td>19.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 faceId faceAttributes.glasses  \\\n",
       "0  00011785-70b5-4325-be59-624722fd4f13              NoGlasses   \n",
       "1  1a148e4d-ece7-4019-a4b7-d098ad66be0b              NoGlasses   \n",
       "2  986ab02c-00a2-4c99-80ba-1c1a0025db45         ReadingGlasses   \n",
       "3  dd6a85d9-23a4-4486-9f65-9fb077ff6037              NoGlasses   \n",
       "4  85d03d31-93b9-4e85-ac91-3d3f945558c5         ReadingGlasses   \n",
       "5  278e1222-dd78-4610-b5e1-a2afb2ff0d84              NoGlasses   \n",
       "\n",
       "   faceAttributes.emotion.neutral  faceAttributes.age faceAttributes.gender  \n",
       "0                           0.184                19.0                  male  \n",
       "1                           0.542                22.0                female  \n",
       "2                           0.000                25.0                  male  \n",
       "3                           0.000                23.0                female  \n",
       "4                           0.019                18.0                female  \n",
       "5                           0.000                19.0                female  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可观察其中几组数据\n",
    "df_face[['faceId','faceAttributes.glasses','faceAttributes.emotion.neutral','faceAttributes.age','faceAttributes.gender']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Azure 认知服务-人脸演示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 试一试：人脸验证(难)\n",
    "\n",
    "人脸相似度？有没有试下？\n",
    "\n",
    ">* API人脸文档中最重要的组成部分：\n",
    " >>* Request URL？\n",
    " >>* Http Method？\n",
    " >>* 参数？\n",
    "\n",
    "----\n",
    ">* 不同的人脸对比数据\n",
    "  >>* 人脸验证关键数据？\n",
    "  >>* 根据哪些数据证明是同一个人？\n",
    "    \n",
    "----\n",
    ">* 具体步骤：\n",
    "  >>* 1、Create 请求成功200 返回空字符串\n",
    "  >>* 2、Add face 请求成功200 返回persistedFaceId\n",
    "  >>* 3、Detect 准备 被检测人 人脸的id\n",
    "  >>* 4、Find similars 返回相似置信度\n",
    "  >>* 附加：get查看facelists\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://api-xyr.cognitiveservices.azure.com/face/v1.0/facelists/yirong15 \n",
      " https://api-xyr.cognitiveservices.azure.com/face/v1.0/facelists/yirong15 \n",
      " https://api-xyr.cognitiveservices.azure.com/face/v1.0/facelists/yirong15\n"
     ]
    }
   ],
   "source": [
    "# 字符串拼接练习\n",
    "faceListId = \"yirong15\"\n",
    "# 1\n",
    "url_01 = \"https://api-xyr.cognitiveservices.azure.com/face/v1.0/facelists/\" + faceListId # string 拼接\n",
    "# 2\n",
    "url_02 = \"https://api-xyr.cognitiveservices.azure.com/face/v1.0/facelists/%s\" %(faceListId)\n",
    "# 3 \n",
    "url_03 = \"https://api-xyr.cognitiveservices.azure.com/face/v1.0/facelists/{}\".format(faceListId)\n",
    "print(url_01,'\\n',url_02,'\\n',url_03)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create facelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "# 1、create  list列表\n",
    "# faceListId\n",
    "faceListId = \"yirong15\" # 学生填写设置人脸列表ID\n",
    "create_facelists_url = \"https://api-xyr.cognitiveservices.azure.com/face/v1.0/facelists/{}\"# 学生填写 ☆ 注意此条url修改\n",
    "subscription_key = \"aa04a3698a2c413b9fce981fdca00510\"\n",
    "assert subscription_key\n",
    "\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "data = {\n",
    "    \"name\": \"19级\",\n",
    "    \"userData\": \"18,男8,女10\",\n",
    "    \"recognitionModel\": \"recognition_03\",\n",
    "    \n",
    "}\n",
    "\n",
    "r_create = requests.put(create_facelists_url.format(faceListId),headers=headers,json=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_create"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b''"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_create.content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get facelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api-xyr.cognitiveservices.azure.com/face/v1.0/facelists/%s\"%(faceListId)# 学生填写\n",
    "r_get_facelist = requests.get(get_facelist_url,headers=headers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [],\n",
       " 'faceListId': 'yirong15',\n",
       " 'name': '19级',\n",
       " 'userData': '18,男8,女10'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_get_facelist.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add face 请求成功200 返回persistedFaceId\n",
    "> 我们通过上面的步骤建好了一个脸的列表，接下来我们要给这个列表添加脸了！把我们想要对比的脸存进列表吧\n",
    "- [添加人脸进列表api文档](https://docs.microsoft.com/zh-cn/rest/api/cognitiveservices/face/facelist/addfacefromurl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#先加一张脸试试\n",
    "# 2、Add face\n",
    "add_face_url =\"https://api-xyr.cognitiveservices.azure.com/face/v1.0/facelists/{}/persistedFaces\"\n",
    "\n",
    "assert subscription_key\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "img_url = \"http://huangjieqi.gitee.io/picture_storage/EdisonQXF.jpg\"\n",
    "\n",
    "params_add_face={\n",
    "    \"userData\":\"许一一\"\n",
    "}\n",
    "\n",
    "r_add_face = requests.post(add_face_url.format(faceListId),headers=headers,params=params_add_face,json={\"url\":img_url})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_add_face.status_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [{'persistedFaceId': '66ee3f8a-0c32-4a9f-a559-723ae11722d9',\n",
       "   'userData': '许一一'}],\n",
       " 'faceListId': 'yirong15',\n",
       " 'name': '19级',\n",
       " 'userData': '18,男8,女10'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api-xyr.cognitiveservices.azure.com/face/v1.0/facelists/{}\".format(faceListId)\n",
    "r_get_facelist = requests.get(get_facelist_url,headers=headers)\n",
    "r_get_facelist.json()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 扩展内容，封装成函数方便多次使用 *\n",
    "> 我们要添加多张脸，但是为了减少代码量，我们可以把代码封装成函数，避免每次都要写一大堆代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 封装成函数方便添加图片/函数——可以重复使用相同的功能\n",
    "def AddFace(img_url=str,userData=str):\n",
    "    add_face_url =\"https://api-xyr.cognitiveservices.azure.com/face/v1.0/facelists/{}/persistedFaces\"\n",
    "    \n",
    "    assert subscription_key\n",
    "    headers = {\n",
    "        # Request headers\n",
    "        'Content-Type': 'application/json',\n",
    "        'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "    }\n",
    "    img_url = img_url\n",
    "\n",
    "    params_add_face={\n",
    "        \"userData\":userData\n",
    "    }\n",
    "    r_add_face = requests.post(add_face_url.format(faceListId),headers=headers,params=params_add_face,json={\"url\":img_url})\n",
    "    return r_add_face.status_code#返回出状态码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/Autumnhui.jpg\",\"丘天惠\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/L-Tony-info.jpg\",\"林嘉茵\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/TLINGP.jpg\",\"汤玲萍\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/WenYanZeng.jpg\",\"曾雯燕\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/XIEIC.jpg\",\"谢依希\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/YuecongYang.png\",\"杨悦聪\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/Zoezhouyu.jpg\",\"周雨\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/crayon-heimi.jpg\",\"刘瑜鹏\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/jiayichen.jpg\",\"陈嘉仪\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/kg2000.jpg\",\"徐旖芊\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/liuxinrujiayou.jpg\",\"刘心如\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/liuyu19.png\",\"刘宇\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/ltco.jpg\",\"李婷\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/lucaszy.jpg\",\"黄智毅\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/pingzi0211.jpg\",\"黄慧文\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/shmimy-cn.jpg\",\"张铭睿\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/yichenting.jpg\",\"陈婷\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/coco022.jpg\",\"洪可凡\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/lujizhi.png\",\"卢继志\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/zzlhyy.jpg\",\"张梓乐\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [{'persistedFaceId': '66ee3f8a-0c32-4a9f-a559-723ae11722d9',\n",
       "   'userData': '许一一'},\n",
       "  {'persistedFaceId': '03be11a7-16ef-4a0c-aac1-c88fd0f1c967',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': 'b826206e-e307-457e-ab2a-c09a805bfb71',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': 'aa6dd339-8fc4-48c3-a96e-18034121fcc7',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': '982c67a0-b209-49f2-833e-ff7f53691886',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': '11ac6510-512f-4f05-951a-13f400207a29',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': '724b2045-36f7-4498-94fc-20e3d7532de2',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': '6ea12e0c-e9c8-4c12-9d08-3dfe239bfbe4',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': '354713db-0bed-4d99-a613-37c66b4bd5e4',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': '890faf46-0d0c-4e1c-ab31-5c8b2984a22a',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': 'e0d0c3f1-792f-4fb8-8734-fa459a7df365',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': '16816139-12e9-44d0-ad3d-d6dbc9aa2ccf',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': '7c348e07-6cf6-454c-80e7-933b20276e54',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': '6b8592f4-efdc-458c-aeb4-1edc8d1afcc7',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': '67be7257-93c4-489b-89dd-654784eabfca',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '09346c1b-5888-41e4-ac49-192422d0a956',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': '4c20c381-5ff6-4d10-9b55-5fcd2520ac94',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '0883e864-2c77-4490-b101-f80c428e8c7e',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '0ac51fc7-ee27-4f79-8e40-f025eaad625b',\n",
       "   'userData': '洪可凡'},\n",
       "  {'persistedFaceId': 'bc1f6f81-7d7c-4089-bbdd-a854ee9d2611',\n",
       "   'userData': '卢继志'},\n",
       "  {'persistedFaceId': '26637ecd-42f9-4be4-adf7-1f97121095b7',\n",
       "   'userData': '张梓乐'}],\n",
       " 'faceListId': 'yirong15',\n",
       " 'name': '19级',\n",
       " 'userData': '18,男8,女10'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api-xyr.cognitiveservices.azure.com/face/v1.0/facelists/yirong15\"\n",
    "r_get_facelist = requests.get(get_facelist_url,headers=headers)\n",
    "r_get_facelist.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'persistedFaceId': '66ee3f8a-0c32-4a9f-a559-723ae11722d9',\n",
       "  'userData': '许一一'},\n",
       " {'persistedFaceId': '03be11a7-16ef-4a0c-aac1-c88fd0f1c967',\n",
       "  'userData': '丘天惠'},\n",
       " {'persistedFaceId': 'b826206e-e307-457e-ab2a-c09a805bfb71',\n",
       "  'userData': '林嘉茵'},\n",
       " {'persistedFaceId': 'aa6dd339-8fc4-48c3-a96e-18034121fcc7',\n",
       "  'userData': '汤玲萍'},\n",
       " {'persistedFaceId': '982c67a0-b209-49f2-833e-ff7f53691886',\n",
       "  'userData': '曾雯燕'},\n",
       " {'persistedFaceId': '11ac6510-512f-4f05-951a-13f400207a29',\n",
       "  'userData': '谢依希'},\n",
       " {'persistedFaceId': '724b2045-36f7-4498-94fc-20e3d7532de2',\n",
       "  'userData': '杨悦聪'},\n",
       " {'persistedFaceId': '6ea12e0c-e9c8-4c12-9d08-3dfe239bfbe4', 'userData': '周雨'},\n",
       " {'persistedFaceId': '354713db-0bed-4d99-a613-37c66b4bd5e4',\n",
       "  'userData': '刘瑜鹏'},\n",
       " {'persistedFaceId': '890faf46-0d0c-4e1c-ab31-5c8b2984a22a',\n",
       "  'userData': '陈嘉仪'},\n",
       " {'persistedFaceId': 'e0d0c3f1-792f-4fb8-8734-fa459a7df365',\n",
       "  'userData': '徐旖芊'},\n",
       " {'persistedFaceId': '16816139-12e9-44d0-ad3d-d6dbc9aa2ccf',\n",
       "  'userData': '刘心如'},\n",
       " {'persistedFaceId': '7c348e07-6cf6-454c-80e7-933b20276e54', 'userData': '刘宇'},\n",
       " {'persistedFaceId': '6b8592f4-efdc-458c-aeb4-1edc8d1afcc7', 'userData': '李婷'},\n",
       " {'persistedFaceId': '67be7257-93c4-489b-89dd-654784eabfca',\n",
       "  'userData': '黄智毅'},\n",
       " {'persistedFaceId': '09346c1b-5888-41e4-ac49-192422d0a956',\n",
       "  'userData': '黄慧文'},\n",
       " {'persistedFaceId': '4c20c381-5ff6-4d10-9b55-5fcd2520ac94',\n",
       "  'userData': '张铭睿'},\n",
       " {'persistedFaceId': '0883e864-2c77-4490-b101-f80c428e8c7e', 'userData': '陈婷'},\n",
       " {'persistedFaceId': '0ac51fc7-ee27-4f79-8e40-f025eaad625b',\n",
       "  'userData': '洪可凡'},\n",
       " {'persistedFaceId': 'bc1f6f81-7d7c-4089-bbdd-a854ee9d2611',\n",
       "  'userData': '卢继志'},\n",
       " {'persistedFaceId': '26637ecd-42f9-4be4-adf7-1f97121095b7',\n",
       "  'userData': '张梓乐'}]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faceId =  r_get_facelist.json()['persistedFaces']\n",
    "faceId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b826206e-e307-457e-ab2a-c09a805bfb71'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 键/值\n",
    "for i in faceId:\n",
    "#     print(i)\n",
    "    if i[\"userData\"] == \"林嘉茵\":\n",
    "        faceId_02 = i['persistedFaceId']\n",
    "faceId_02\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'persistedFaceId': '66ee3f8a-0c32-4a9f-a559-723ae11722d9',\n",
       "  'userData': '许一一'},\n",
       " {'persistedFaceId': '03be11a7-16ef-4a0c-aac1-c88fd0f1c967',\n",
       "  'userData': '丘天惠'},\n",
       " {'persistedFaceId': 'b826206e-e307-457e-ab2a-c09a805bfb71',\n",
       "  'userData': '林嘉茵'},\n",
       " {'persistedFaceId': 'aa6dd339-8fc4-48c3-a96e-18034121fcc7',\n",
       "  'userData': '汤玲萍'},\n",
       " {'persistedFaceId': '982c67a0-b209-49f2-833e-ff7f53691886',\n",
       "  'userData': '曾雯燕'},\n",
       " {'persistedFaceId': '11ac6510-512f-4f05-951a-13f400207a29',\n",
       "  'userData': '谢依希'},\n",
       " {'persistedFaceId': '724b2045-36f7-4498-94fc-20e3d7532de2',\n",
       "  'userData': '杨悦聪'},\n",
       " {'persistedFaceId': '6ea12e0c-e9c8-4c12-9d08-3dfe239bfbe4', 'userData': '周雨'},\n",
       " {'persistedFaceId': '354713db-0bed-4d99-a613-37c66b4bd5e4',\n",
       "  'userData': '刘瑜鹏'},\n",
       " {'persistedFaceId': '890faf46-0d0c-4e1c-ab31-5c8b2984a22a',\n",
       "  'userData': '陈嘉仪'},\n",
       " {'persistedFaceId': 'e0d0c3f1-792f-4fb8-8734-fa459a7df365',\n",
       "  'userData': '徐旖芊'},\n",
       " {'persistedFaceId': '16816139-12e9-44d0-ad3d-d6dbc9aa2ccf',\n",
       "  'userData': '刘心如'},\n",
       " {'persistedFaceId': '7c348e07-6cf6-454c-80e7-933b20276e54', 'userData': '刘宇'},\n",
       " {'persistedFaceId': '6b8592f4-efdc-458c-aeb4-1edc8d1afcc7', 'userData': '李婷'},\n",
       " {'persistedFaceId': '67be7257-93c4-489b-89dd-654784eabfca',\n",
       "  'userData': '黄智毅'},\n",
       " {'persistedFaceId': '09346c1b-5888-41e4-ac49-192422d0a956',\n",
       "  'userData': '黄慧文'},\n",
       " {'persistedFaceId': '4c20c381-5ff6-4d10-9b55-5fcd2520ac94',\n",
       "  'userData': '张铭睿'},\n",
       " {'persistedFaceId': '0883e864-2c77-4490-b101-f80c428e8c7e', 'userData': '陈婷'},\n",
       " {'persistedFaceId': '0ac51fc7-ee27-4f79-8e40-f025eaad625b',\n",
       "  'userData': '洪可凡'},\n",
       " {'persistedFaceId': 'bc1f6f81-7d7c-4089-bbdd-a854ee9d2611',\n",
       "  'userData': '卢继志'},\n",
       " {'persistedFaceId': '26637ecd-42f9-4be4-adf7-1f97121095b7',\n",
       "  'userData': '张梓乐'}]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faceId =  r_get_facelist.json()['persistedFaces']\n",
    "faceId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67be7257-93c4-489b-89dd-654784eabfca\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'67be7257-93c4-489b-89dd-654784eabfca'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for item in faceId:\n",
    "#     print(item)\n",
    "    if item['userData'] == '黄智毅':\n",
    "        print(item['persistedFaceId'])\n",
    "        delate_face = item['persistedFaceId']\n",
    "delate_face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### delate face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detect face 删除列表内人脸id\n",
    "faceListId = \"yirong15\"\n",
    "persistedFaceId = \"67be7257-93c4-489b-89dd-654784eabfca\"\n",
    "delete_face_url = \"https://api-xyr.cognitiveservices.azure.com/face/v1.0/facelists/{}/persistedFaces/{}\"\n",
    "\n",
    "assert subscription_key\n",
    "# 例如：删除黄志毅： {'persistedFaceId': '69103b48-b6c4-4f58-8ac1-4c8b84e56bc1','userData': '黄智毅'},\n",
    "\n",
    "\n",
    "persistedFaceId = delate_face\n",
    "# 直接取上面获得的ID{'persistedFaceId': 'f18450d3-60d2-45f3-a69e-783574dc3ce8'} \n",
    "\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "\n",
    "# 注意requests请求为delete\n",
    "r_delete_face = requests.delete(delete_face_url.format(faceListId,persistedFaceId),headers=headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_delete_face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [{'persistedFaceId': '66ee3f8a-0c32-4a9f-a559-723ae11722d9',\n",
       "   'userData': '许一一'},\n",
       "  {'persistedFaceId': '03be11a7-16ef-4a0c-aac1-c88fd0f1c967',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': 'b826206e-e307-457e-ab2a-c09a805bfb71',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': 'aa6dd339-8fc4-48c3-a96e-18034121fcc7',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': '982c67a0-b209-49f2-833e-ff7f53691886',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': '11ac6510-512f-4f05-951a-13f400207a29',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': '724b2045-36f7-4498-94fc-20e3d7532de2',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': '6ea12e0c-e9c8-4c12-9d08-3dfe239bfbe4',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': '354713db-0bed-4d99-a613-37c66b4bd5e4',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': '890faf46-0d0c-4e1c-ab31-5c8b2984a22a',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': 'e0d0c3f1-792f-4fb8-8734-fa459a7df365',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': '16816139-12e9-44d0-ad3d-d6dbc9aa2ccf',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': '7c348e07-6cf6-454c-80e7-933b20276e54',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': '6b8592f4-efdc-458c-aeb4-1edc8d1afcc7',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': '09346c1b-5888-41e4-ac49-192422d0a956',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': '4c20c381-5ff6-4d10-9b55-5fcd2520ac94',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '0883e864-2c77-4490-b101-f80c428e8c7e',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': '0ac51fc7-ee27-4f79-8e40-f025eaad625b',\n",
       "   'userData': '洪可凡'},\n",
       "  {'persistedFaceId': 'bc1f6f81-7d7c-4089-bbdd-a854ee9d2611',\n",
       "   'userData': '卢继志'},\n",
       "  {'persistedFaceId': '26637ecd-42f9-4be4-adf7-1f97121095b7',\n",
       "   'userData': '张梓乐'}],\n",
       " 'faceListId': 'yirong15',\n",
       " 'name': '19级',\n",
       " 'userData': '18,男8,女10'}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api-xyr.cognitiveservices.azure.com/face/v1.0/facelists/yirong15\"\n",
    "r_get_facelist = requests.get(get_facelist_url,headers=headers)\n",
    "r_get_facelist.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find similars 返回相似置信度\n",
    "- [监测人脸相似度api文档](https://docs.microsoft.com/zh-cn/rest/api/cognitiveservices/face/face/findsimilar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'faceId': 'c5266ffe-16ab-4aa9-9c76-9d20186677d0',\n",
       "  'faceRectangle': {'top': 264, 'left': 207, 'width': 277, 'height': 277}}]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detect 检测人脸的id\n",
    "# replace <My Endpoint String> with the string from your endpoint URL\n",
    "face_api_url = 'https://api-xyr.cognitiveservices.azure.com/face/v1.0/detect'\n",
    "\n",
    "# 请求正文\n",
    "image_url = 'http://huangjieqi.gitee.io/picture_storage/hjq.jpg'\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "\n",
    "# 请求参数\n",
    "params = {\n",
    "    'returnFaceId': 'true',\n",
    "    'returnFaceLandmarks': 'false',\n",
    "    # 选择model\n",
    "    'recognitionModel':'recognition_03',#此参数需与facelist参数一致\n",
    "    'detectionModel':'detection_01',\n",
    "    # 可选参数,请仔细阅读API文档\n",
    "    'returnFaceAttributes': '',\n",
    "}\n",
    "\n",
    "response = requests.post(face_api_url, params=params,\n",
    "                         headers=headers, json={\"url\": image_url})\n",
    "# json.dumps 将json--->字符串\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "findsimilars_url = \"https://api-xyr.cognitiveservices.azure.com/face/v1.0/findsimilars\"\n",
    "\n",
    "# 请求正文 faceId需要先检测一张照片获取\n",
    "data_findsimilars = {\n",
    "    \"faceId\":\"c5266ffe-16ab-4aa9-9c76-9d20186677d0\",#取上方的faceID\n",
    "    \"faceListId\": faceListId,\n",
    "#     \"faceIds\":faceId_02,\n",
    "    \"maxNumOfCandidatesReturned\": 10,\n",
    "    \"mode\": \"matchFace\"#matchPerson #一种为验证模式，一种为相似值模式\n",
    "    }\n",
    "\n",
    "r_findsimilars = requests.post(findsimilars_url,headers=headers,json=data_findsimilars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_findsimilars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'persistedFaceId': 'e0d0c3f1-792f-4fb8-8734-fa459a7df365',\n",
       "  'confidence': 0.29269},\n",
       " {'persistedFaceId': '6b8592f4-efdc-458c-aeb4-1edc8d1afcc7',\n",
       "  'confidence': 0.20908},\n",
       " {'persistedFaceId': '4c20c381-5ff6-4d10-9b55-5fcd2520ac94',\n",
       "  'confidence': 0.17849},\n",
       " {'persistedFaceId': '890faf46-0d0c-4e1c-ab31-5c8b2984a22a',\n",
       "  'confidence': 0.16209},\n",
       " {'persistedFaceId': '16816139-12e9-44d0-ad3d-d6dbc9aa2ccf',\n",
       "  'confidence': 0.15023},\n",
       " {'persistedFaceId': '0ac51fc7-ee27-4f79-8e40-f025eaad625b',\n",
       "  'confidence': 0.101},\n",
       " {'persistedFaceId': 'b826206e-e307-457e-ab2a-c09a805bfb71',\n",
       "  'confidence': 0.10034},\n",
       " {'persistedFaceId': '26637ecd-42f9-4be4-adf7-1f97121095b7',\n",
       "  'confidence': 0.0999},\n",
       " {'persistedFaceId': '66ee3f8a-0c32-4a9f-a559-723ae11722d9',\n",
       "  'confidence': 0.09955},\n",
       " {'persistedFaceId': '03be11a7-16ef-4a0c-aac1-c88fd0f1c967',\n",
       "  'confidence': 0.09405}]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_findsimilars.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>persistedFaceId</th>\n",
       "      <th>userData</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>66ee3f8a-0c32-4a9f-a559-723ae11722d9</td>\n",
       "      <td>许一一</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03be11a7-16ef-4a0c-aac1-c88fd0f1c967</td>\n",
       "      <td>丘天惠</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b826206e-e307-457e-ab2a-c09a805bfb71</td>\n",
       "      <td>林嘉茵</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aa6dd339-8fc4-48c3-a96e-18034121fcc7</td>\n",
       "      <td>汤玲萍</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>982c67a0-b209-49f2-833e-ff7f53691886</td>\n",
       "      <td>曾雯燕</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11ac6510-512f-4f05-951a-13f400207a29</td>\n",
       "      <td>谢依希</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>724b2045-36f7-4498-94fc-20e3d7532de2</td>\n",
       "      <td>杨悦聪</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6ea12e0c-e9c8-4c12-9d08-3dfe239bfbe4</td>\n",
       "      <td>周雨</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>354713db-0bed-4d99-a613-37c66b4bd5e4</td>\n",
       "      <td>刘瑜鹏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>890faf46-0d0c-4e1c-ab31-5c8b2984a22a</td>\n",
       "      <td>陈嘉仪</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>e0d0c3f1-792f-4fb8-8734-fa459a7df365</td>\n",
       "      <td>徐旖芊</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16816139-12e9-44d0-ad3d-d6dbc9aa2ccf</td>\n",
       "      <td>刘心如</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7c348e07-6cf6-454c-80e7-933b20276e54</td>\n",
       "      <td>刘宇</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6b8592f4-efdc-458c-aeb4-1edc8d1afcc7</td>\n",
       "      <td>李婷</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>09346c1b-5888-41e4-ac49-192422d0a956</td>\n",
       "      <td>黄慧文</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4c20c381-5ff6-4d10-9b55-5fcd2520ac94</td>\n",
       "      <td>张铭睿</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0883e864-2c77-4490-b101-f80c428e8c7e</td>\n",
       "      <td>陈婷</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0ac51fc7-ee27-4f79-8e40-f025eaad625b</td>\n",
       "      <td>洪可凡</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>bc1f6f81-7d7c-4089-bbdd-a854ee9d2611</td>\n",
       "      <td>卢继志</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>26637ecd-42f9-4be4-adf7-1f97121095b7</td>\n",
       "      <td>张梓乐</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         persistedFaceId userData\n",
       "0   66ee3f8a-0c32-4a9f-a559-723ae11722d9      许一一\n",
       "1   03be11a7-16ef-4a0c-aac1-c88fd0f1c967      丘天惠\n",
       "2   b826206e-e307-457e-ab2a-c09a805bfb71      林嘉茵\n",
       "3   aa6dd339-8fc4-48c3-a96e-18034121fcc7      汤玲萍\n",
       "4   982c67a0-b209-49f2-833e-ff7f53691886      曾雯燕\n",
       "5   11ac6510-512f-4f05-951a-13f400207a29      谢依希\n",
       "6   724b2045-36f7-4498-94fc-20e3d7532de2      杨悦聪\n",
       "7   6ea12e0c-e9c8-4c12-9d08-3dfe239bfbe4       周雨\n",
       "8   354713db-0bed-4d99-a613-37c66b4bd5e4      刘瑜鹏\n",
       "9   890faf46-0d0c-4e1c-ab31-5c8b2984a22a      陈嘉仪\n",
       "10  e0d0c3f1-792f-4fb8-8734-fa459a7df365      徐旖芊\n",
       "11  16816139-12e9-44d0-ad3d-d6dbc9aa2ccf      刘心如\n",
       "12  7c348e07-6cf6-454c-80e7-933b20276e54       刘宇\n",
       "13  6b8592f4-efdc-458c-aeb4-1edc8d1afcc7       李婷\n",
       "14  09346c1b-5888-41e4-ac49-192422d0a956      黄慧文\n",
       "15  4c20c381-5ff6-4d10-9b55-5fcd2520ac94      张铭睿\n",
       "16  0883e864-2c77-4490-b101-f80c428e8c7e       陈婷\n",
       "17  0ac51fc7-ee27-4f79-8e40-f025eaad625b      洪可凡\n",
       "18  bc1f6f81-7d7c-4089-bbdd-a854ee9d2611      卢继志\n",
       "19  26637ecd-42f9-4be4-adf7-1f97121095b7      张梓乐"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#facelist里面的数据\n",
    "faceListId_df = pd.json_normalize(r_get_facelist.json()[\"persistedFaces\"])# 升级pandas才能运行\n",
    "faceListId_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>persistedFaceId</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e0d0c3f1-792f-4fb8-8734-fa459a7df365</td>\n",
       "      <td>0.29269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6b8592f4-efdc-458c-aeb4-1edc8d1afcc7</td>\n",
       "      <td>0.20908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4c20c381-5ff6-4d10-9b55-5fcd2520ac94</td>\n",
       "      <td>0.17849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>890faf46-0d0c-4e1c-ab31-5c8b2984a22a</td>\n",
       "      <td>0.16209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16816139-12e9-44d0-ad3d-d6dbc9aa2ccf</td>\n",
       "      <td>0.15023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0ac51fc7-ee27-4f79-8e40-f025eaad625b</td>\n",
       "      <td>0.10100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b826206e-e307-457e-ab2a-c09a805bfb71</td>\n",
       "      <td>0.10034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26637ecd-42f9-4be4-adf7-1f97121095b7</td>\n",
       "      <td>0.09990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>66ee3f8a-0c32-4a9f-a559-723ae11722d9</td>\n",
       "      <td>0.09955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>03be11a7-16ef-4a0c-aac1-c88fd0f1c967</td>\n",
       "      <td>0.09405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        persistedFaceId  confidence\n",
       "0  e0d0c3f1-792f-4fb8-8734-fa459a7df365     0.29269\n",
       "1  6b8592f4-efdc-458c-aeb4-1edc8d1afcc7     0.20908\n",
       "2  4c20c381-5ff6-4d10-9b55-5fcd2520ac94     0.17849\n",
       "3  890faf46-0d0c-4e1c-ab31-5c8b2984a22a     0.16209\n",
       "4  16816139-12e9-44d0-ad3d-d6dbc9aa2ccf     0.15023\n",
       "5  0ac51fc7-ee27-4f79-8e40-f025eaad625b     0.10100\n",
       "6  b826206e-e307-457e-ab2a-c09a805bfb71     0.10034\n",
       "7  26637ecd-42f9-4be4-adf7-1f97121095b7     0.09990\n",
       "8  66ee3f8a-0c32-4a9f-a559-723ae11722d9     0.09955\n",
       "9  03be11a7-16ef-4a0c-aac1-c88fd0f1c967     0.09405"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 返回相似度的数据\n",
    "find_df = pd.json_normalize(r_findsimilars.json())# 升级pandas才能运行\n",
    "find_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>persistedFaceId</th>\n",
       "      <th>userData</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e0d0c3f1-792f-4fb8-8734-fa459a7df365</td>\n",
       "      <td>徐旖芊</td>\n",
       "      <td>0.29269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6b8592f4-efdc-458c-aeb4-1edc8d1afcc7</td>\n",
       "      <td>李婷</td>\n",
       "      <td>0.20908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4c20c381-5ff6-4d10-9b55-5fcd2520ac94</td>\n",
       "      <td>张铭睿</td>\n",
       "      <td>0.17849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>890faf46-0d0c-4e1c-ab31-5c8b2984a22a</td>\n",
       "      <td>陈嘉仪</td>\n",
       "      <td>0.16209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16816139-12e9-44d0-ad3d-d6dbc9aa2ccf</td>\n",
       "      <td>刘心如</td>\n",
       "      <td>0.15023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0ac51fc7-ee27-4f79-8e40-f025eaad625b</td>\n",
       "      <td>洪可凡</td>\n",
       "      <td>0.10100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b826206e-e307-457e-ab2a-c09a805bfb71</td>\n",
       "      <td>林嘉茵</td>\n",
       "      <td>0.10034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>26637ecd-42f9-4be4-adf7-1f97121095b7</td>\n",
       "      <td>张梓乐</td>\n",
       "      <td>0.09990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>66ee3f8a-0c32-4a9f-a559-723ae11722d9</td>\n",
       "      <td>许一一</td>\n",
       "      <td>0.09955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03be11a7-16ef-4a0c-aac1-c88fd0f1c967</td>\n",
       "      <td>丘天惠</td>\n",
       "      <td>0.09405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        persistedFaceId userData  confidence\n",
       "4  e0d0c3f1-792f-4fb8-8734-fa459a7df365      徐旖芊     0.29269\n",
       "6  6b8592f4-efdc-458c-aeb4-1edc8d1afcc7       李婷     0.20908\n",
       "7  4c20c381-5ff6-4d10-9b55-5fcd2520ac94      张铭睿     0.17849\n",
       "3  890faf46-0d0c-4e1c-ab31-5c8b2984a22a      陈嘉仪     0.16209\n",
       "5  16816139-12e9-44d0-ad3d-d6dbc9aa2ccf      刘心如     0.15023\n",
       "8  0ac51fc7-ee27-4f79-8e40-f025eaad625b      洪可凡     0.10100\n",
       "2  b826206e-e307-457e-ab2a-c09a805bfb71      林嘉茵     0.10034\n",
       "9  26637ecd-42f9-4be4-adf7-1f97121095b7      张梓乐     0.09990\n",
       "0  66ee3f8a-0c32-4a9f-a559-723ae11722d9      许一一     0.09955\n",
       "1  03be11a7-16ef-4a0c-aac1-c88fd0f1c967      丘天惠     0.09405"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(faceListId_df, find_df,how='inner', on='persistedFaceId').sort_values(by=\"confidence\",ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 设计人脸识别门禁/打卡/签到 小程序"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Face++ FaceSets 实践\n",
    "\n",
    "\n",
    ">* 1. FaceSet Create\n",
    ">* 2. FaceSet GetDetail\n",
    ">* 3. FaceSet AddFace\n",
    ">* 4. FaceSet RemoveFace\n",
    ">* 5. FaceSet Update\n",
    ">* 6. Compare Face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备工作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "api_secret = \"jgQm9OrUKuPJJZPO5Dkh0ZreE_Zi7xrA\"\n",
    "api_key = '4Yk7z309UlliG_gR1xI2Z4_iYBLUqcj4'\n",
    "\n",
    "\n",
    "BASE_URL = 'https://api-cn.faceplusplus.com/facepp/v3/detect' \n",
    "img_url = 'https://www.apple.com.cn/newsroom/images/live-action/keynote-september-2020/apple_apple-event-keynote_tim_09152020_big.jpg.large_2x.jpg'\n",
    "\n",
    "\n",
    "headers = {\n",
    "    'Content-Type': 'application/json',\n",
    "}\n",
    "\n",
    "payload = {\n",
    "    \"image_url\":img_url,\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'return_attributes':'gender,age,smiling,emotion', \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = requests.post(BASE_URL, params=payload, headers=headers)\n",
    "r.status_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'{\"request_id\":\"1603525929,090f4108-4851-4d46-8872-3a9fef85d2f0\",\"time_used\":2160,\"faces\":[{\"face_token\":\"d8d4197ca2b22b2554a9275f96f8b632\",\"face_rectangle\":{\"top\":139,\"left\":583,\"width\":140,\"height\":140},\"attributes\":{\"gender\":{\"value\":\"Male\"},\"age\":{\"value\":66},\"smile\":{\"value\":28.454,\"threshold\":50.000},\"emotion\":{\"anger\":0.040,\"disgust\":37.393,\"fear\":0.040,\"happiness\":24.038,\"neutral\":37.699,\"sadness\":0.735,\"surprise\":0.054}}}],\"image_id\":\"AfGvmVi3qesqsPIfULDZkw==\",\"face_num\":1}\\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet Create（创建人脸集合）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. FaceSet Create\n",
    "import requests,json\n",
    "\n",
    "display_name = \"网新三班人脸集合01\"\n",
    "outer_id = \"00002\"\n",
    "user_data = \"41人，9男生，32女生\"\n",
    "\n",
    "CreateFace_Url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/create\"\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'display_name':display_name,\n",
    "    'outer_id':outer_id,\n",
    "    'user_data':user_data\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(CreateFace_Url, params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': 'e8bfdedf8980bc0a114d4d1771bbe16d',\n",
       " 'time_used': 158,\n",
       " 'face_count': 0,\n",
       " 'face_added': 0,\n",
       " 'request_id': '1603525994,18e33086-6935-4456-9adf-1ea2149a512d',\n",
       " 'outer_id': '00002',\n",
       " 'failure_detail': []}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet GetDetail（获取人脸集合信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "GetDetail_Url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/getdetail\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'outer_id':outer_id,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(GetDetail_Url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': 'e8bfdedf8980bc0a114d4d1771bbe16d',\n",
       " 'tags': '',\n",
       " 'time_used': 120,\n",
       " 'user_data': '41人，9男生，32女生',\n",
       " 'display_name': '网新三班人脸集合01',\n",
       " 'face_tokens': [],\n",
       " 'face_count': 0,\n",
       " 'request_id': '1603526218,7b5a6021-0ece-4867-a33d-43c9656ee517',\n",
       " 'outer_id': '00002'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet AddFace（增加人脸信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "AddFace_url = \" https://api-cn.faceplusplus.com/facepp/v3/faceset/addface\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'faceset_token':'e8bfdedf8980bc0a114d4d1771bbe16d',\n",
    "    'face_tokens':'d8d4197ca2b22b2554a9275f96f8b632',\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(AddFace_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': 'e8bfdedf8980bc0a114d4d1771bbe16d',\n",
       " 'time_used': 677,\n",
       " 'face_count': 1,\n",
       " 'face_added': 1,\n",
       " 'request_id': '1603526465,9010572c-5467-4624-a42f-3a3003637eaf',\n",
       " 'outer_id': '00002',\n",
       " 'failure_detail': []}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet RemoveFace（移除人脸信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "RemoveFace_url = \" https://api-cn.faceplusplus.com/facepp/v3/faceset/removeface\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'faceset_token':'e8bfdedf8980bc0a114d4d1771bbe16d',\n",
    "    'face_tokens':'d8d4197ca2b22b2554a9275f96f8b632',\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(RemoveFace_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': 'e8bfdedf8980bc0a114d4d1771bbe16d',\n",
       " 'face_removed': 1,\n",
       " 'time_used': 176,\n",
       " 'face_count': 0,\n",
       " 'request_id': '1603526510,daaaad39-e75c-4a67-8252-a19719f6af49',\n",
       " 'outer_id': '00002',\n",
       " 'failure_detail': []}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet Update（更新人脸集合信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "Update_url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/update\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'faceset_token':'e8bfdedf8980bc0a114d4d1771bbe16d',\n",
    "    'user_data':\"41人，9男生，32女生\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Update_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': 'e8bfdedf8980bc0a114d4d1771bbe16d',\n",
       " 'request_id': '1603526578,2c9d50cd-8c41-41dd-8b3b-6e31d2087c66',\n",
       " 'time_used': 70,\n",
       " 'outer_id': '00002'}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Face（对比人脸相似度）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "liudehua01 = \"https://gss0.baidu.com/9fo3dSag_xI4khGko9WTAnF6hhy/zhidao/pic/item/7c1ed21b0ef41bd57f7f20ff57da81cb39db3d89.jpg\"\n",
    "liudehua02 = \"https://tse3-mm.cn.bing.net/th/id/OIP.Xz3HbYZeNrdUnGJ7vXNzsQHaKO?pid=Api&rs=1\"\n",
    "wangzulan = \"https://tse3-mm.cn.bing.net/th/id/OIP.ZnXeGoVYT4jQudiPOGZn3QAAAA?pid=Api&rs=1\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方案1:直接对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "Compare_url = \"https://api-cn.faceplusplus.com/facepp/v3/compare\"\n",
    "\n",
    "payload ={\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'image_url1':liudehua01,\n",
    "    'image_url2':wangzulan\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Compare_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faces1': [{'face_rectangle': {'width': 824,\n",
       "    'top': 871,\n",
       "    'left': 1114,\n",
       "    'height': 824},\n",
       "   'face_token': '2de5bbfaab9ba044719911b424e3a5a2'}],\n",
       " 'faces2': [{'face_rectangle': {'width': 86,\n",
       "    'top': 91,\n",
       "    'left': 65,\n",
       "    'height': 86},\n",
       "   'face_token': '020d74bffe962f065be165cf32d462a0'}],\n",
       " 'time_used': 4929,\n",
       " 'thresholds': {'1e-3': 62.327, '1e-5': 73.975, '1e-4': 69.101},\n",
       " 'confidence': 26.085,\n",
       " 'image_id2': 'g6kg8zfyOouG6ftP+GvEfg==',\n",
       " 'image_id1': 'KIOXEC2V/MyL4zuopAcNig==',\n",
       " 'request_id': '1603526731,39ef7334-fa49-4b6f-9437-eeaf27c29f8f'}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方案2:与人脸集合进行对比"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 面部检测(获取face_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "Detect_url = 'https://api-cn.faceplusplus.com/facepp/v3/detect' \n",
    "img_url = liudehua01\n",
    "\n",
    "payload = {\n",
    "    \"image_url\":img_url,\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'return_attributes':'gender,age,smiling,emotion', \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Detect_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'request_id': '1603527017,b391ce0f-2e2e-4341-a834-a908069e257c',\n",
       " 'time_used': 2179,\n",
       " 'faces': [{'face_token': 'ab0db9e3a20ffa11defec3531bf9b9c3',\n",
       "   'face_rectangle': {'top': 871, 'left': 1114, 'width': 824, 'height': 824},\n",
       "   'attributes': {'gender': {'value': 'Male'},\n",
       "    'age': {'value': 59},\n",
       "    'smile': {'value': 99.998, 'threshold': 50.0},\n",
       "    'emotion': {'anger': 0.0,\n",
       "     'disgust': 0.047,\n",
       "     'fear': 0.0,\n",
       "     'happiness': 99.945,\n",
       "     'neutral': 0.0,\n",
       "     'sadness': 0.007,\n",
       "     'surprise': 0.0}}}],\n",
       " 'image_id': 'KIOXEC2V/MyL4zuopAcNig==',\n",
       " 'face_num': 1}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 加入人脸集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 人脸识别社会/政治使用思考\n",
    "> 人脸识别的 API比较\n",
    ">> * [Face Off: Confronting Bias in Face Recognition AI](https://www.kairos.com/blog/face-off-confronting-bias-in-face-recognition-ai)\n",
    ">> * [为什么世界现在需要道德的面部识别](https://www.kairos.com/blog/why-the-world-needs-ethical-facial-recognition-now)\n",
    "    * 到2023年，全球人脸识别市场的价值估计接近100亿美元，复合年增长率为16.8％。市场背后的主要增长动力是监视市场的发展以及生物识别技术领域的政府支出。但是，面部识别的使用在其他领域也有很大贡献，例如帮助企业打击消费者欺诈，满足动态法规遵从性以及提供可获利的客户体验。\n",
    "\n",
    "-----\n",
    "> 人脸识别的偏差及API 政治经济学 \n",
    ">> * [对抗：面对面部识别AI中的偏见](https://www.kairos.com/blog/face-off-confronting-bias-in-face-recognition-ai)\n",
    "    * 发生了什么？\n",
    "        * “编码注视”或算法偏差的研究: 浅肤色男性的错误率为0.8％ ?  深色皮肤女性的错误率高达34.7％  ? (Microsoft，IBM和Face ++)\n",
    "    * 用户的反应范围从惊奇和赞美到不悦和冒犯\n",
    "        * 我们承认目前提供的种族分类（黑人，白人，亚裔，西班牙裔，其他则不足以代表丰富多样，发展迅速的文化和种族挂毯\n",
    "    * 该怎么办？\n",
    "        * 改善数据\n",
    "        * 寻求持续的反馈    \n",
    "        \n",
    ">>  * [科技行业没有应对面部识别偏见的计划](https://www.theverge.com/2018/7/26/17616290/facial-recognition-ai-bias-benchmark-test) \n",
    ">>> 公司在做什么？(一些高科技大公司支持基准和法规)     \n",
    ">>> 我们如何解决偏见问题？    \n",
    ">>> 偏差不是唯一的问题\n",
    "\n",
    ">> * [面部识别尚未准备好给执法部门使用](https://techcrunch.com/2018/06/25/facial-recognition-software-is-not-ready-for-use-by-law-enforcement/)\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 计算机视觉\n",
    "\n",
    "* 学习并完成所有Azure computer version 的API调用\n",
    "> * 分析远程图像    \n",
    "> * 分析本地图片    \n",
    "> * 生成缩略图    \n",
    "> * 提取印刷体文本和手写文本    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析远程图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"categories\": [{\"name\": \"building_\", \"score\": 0.79296875, \"detail\": {\"landmarks\": []}}, {\"name\": \"others_\", \"score\": 0.00390625}, {\"name\": \"outdoor_\", \"score\": 0.00390625, \"detail\": {\"landmarks\": []}}], \"color\": {\"dominantColorForeground\": \"Blue\", \"dominantColorBackground\": \"Brown\", \"dominantColors\": [\"Brown\"], \"accentColor\": \"1767B4\", \"isBwImg\": false, \"isBWImg\": false}, \"description\": {\"tags\": [\"water\", \"outdoor\", \"building\", \"light\", \"background\", \"city\", \"lit\", \"river\", \"night\", \"body\", \"colorful\", \"tall\", \"boat\", \"view\", \"large\", \"bridge\", \"tower\", \"green\", \"clock\", \"lake\", \"ocean\", \"sun\", \"flying\", \"sunset\", \"pole\", \"traffic\", \"street\", \"blue\", \"plane\", \"airplane\", \"air\"], \"captions\": [{\"text\": \"a lit up city at night\", \"confidence\": 0.8467719096540578}]}, \"requestId\": \"ace94d14-6e78-4b45-a14b-0b82ad24b446\", \"metadata\": {\"height\": 1147, \"width\": 720, \"format\": \"Jpeg\"}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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bAGkeLmnmJLQgVDPPLQnj3qYM+A3NjieRSNC/eYLBE2GmA39FOVOAatn7LLztQhSTgndbYVOZUaoUI4xCk8ISDKGTmVoXnCoX/ODBC8qDc2aUFpXReMA6CeACkzHBIVOQGMoAc2ttb75wCZOpIpK5h+Hzlt/JBiQJC6sgSzJRaLOwUCbwUQVs0KLQtamRujjtNTHzP/a6kqZLz3DQBE0KTWczNz4Bk/GhMtezg8tgKLtFDF0EyxhfgjQ3wRyX838G6ANXmrcXQD5uElVLvGeZefFkOcmkRcLLVibXAKFlChb0nv1+kZJlVE1v8OjkHnr/grdqhRYB8m7WR4O6X7qPOyX7mC481z5fcUZfcB5lhC4hLXvmbfxZyayPaHETH+MbtFh8p7igNrAw2UjNfdP6j2W8KzgSQgvchlsHaVSdgImm1vKc9bbaS4lQM23Vpb+JmLcYQNYBVHWmTsdgwHhpRpKeQgSXiy/U6OPhBJISIYMewoy7FlJp2RNj8cHjaZLvMSYBtHjhIlDg/OaKeivuU6Ro5lnNuar+7Jb3JFxogeHeXAK5H6bq4SIbnluWwQRfvsKCFoprdzw47ThRvY8XgRaeuyYTByQQ9XB0ab2Dh854V8FzcJXgcLVS7OJpmKJW92W5xuIaxMuwvSDRUtTqeK+blRE/GQOH6TaVHzV4lvy+VHWSAc/4djBF934Temf3vCRjGpy0kRVpuRBZTCDhusAY5Ftp4FXHY6xobPlnf/kxq7/7KZ8UzYhPmv1u+0J3d800aMOvXrQQuKgMMQY2M6ndUqSGHsyz+rMVinvgI1YVjSlq78TTITgjwZ7OiBqwZsjgXJHh4CqaTsTTloIsjVUyTZhCTG00wZLVQisej9PFGbE37BbXFll8gRBtgrVI9ryqE3QkxOC67ZkmVajehZ/sJsH6weietpHAKdJxV7wjFgtrPGkSWrU4Y5oIzau3+Yvvfg+9d4f3//b/ZrVcmEZR12zhWRtJ0jHKhZLL72MYQ9dc/W8DVzvNQzerW4sOVgYTH5ZAeootTGzAHOvi87HqQGNamlm0dgfmknL9WkImKzeLDWjVNYZHb0UWqwOLRZfKIi2lJzGENlvAYAqXtKbqXe6Sar2GYXUMCV6FLKT5NmJFINT+GO8yYShhuX3FS4ZqxpCeBjGJm+0SzFM15nMPDxBZaLJmvnaPB398g80X8PZ0j/X2i95ZRrzTvm9xzhCFjvMuK4ovM1nmDISOF0Sqx033Y2WBCdvInBplAwPbpmVIMfS7g/m6hyxJUzBsabTtAeZDOKhfB2dptYjhnyjCLPYPK6JFsgBRKNXMcFWlijKJUItpFBGrQkFsXw+KYbJyeSQeUpFiDoJQKFJzUaUEgY1zq9QsbGQgiagVXBaZepFlpIfiHb7AxV9b/D4wx2BSWKvFyAyxFppUHv3Zn3LnQlkfnfD5n/6xCZSPKRbwcizr8r/AavH38b+XP7P9UOL35sN3+sTzck5ujoMh9/4WwSXHiWJzEq+KLmUcm2dj1BqIrJDUc7mBy4u8vn5fcR2O6UqhuM1bUrXGYI1/moDWkOiKYVdLXBvEKSkWUoSi3pNabGcAk8YIo/TYlNKQUjJDsQfxBtNLuvvqsTeT2NB0io3dyAkySivqptuwqWGd4V2O0aJotGrjztk/cPsX3+Pxyxl+8UNcbboZjX6QN0Xsg4B9pWLfExGhtc6sob3UJxv3de3Gnsebf/NnjQa5uzHSrYNEiCXeV2MVErsRz9/Dlt6EpZCdbgdeB/NnwUIPFHHN5HEnqf6veMm0d4ENuMniU6ZxtFgLm4h6wUdBmHJnom7C4ncvDfdqDR2YICQzXFCRSAmJj80J6TFGH34yp0RzgFiaz7IqFWSiMA0l3lk07ibO0lknP/0xz64LZ9cWjh49pajVwcHkQaJq3quWXPQi1edU6Y1O4owQ2mWQKt81ScQEtYpr86RP0MOFGuWyNi0RH+3xDqJuXl3TIW4dPANStJiVyHmT4yiqHhqKzJK4kB12HZ4GE+P/lsHPBUpDmniQ1xGVKpMALDSq9Y1KhEKUCWHJ0uvF+cWacXoJN4kbDPR7rZ5GLHBkPlIq/SfPl3YtrJFJETP5VgVcEtMgQVovJ3KciIZ22DfDTgl2tfKL9ydWG+H2NJGmUu35GZfLAjVl30HwPycvvMl5qLjziOl8p4nTM+KamYENWl9KqxU3rdo6vot3tMhsO806PZq3BNjYsuTM/19iEqmKD7sOZ7pigdYiimhh0WoDyr0gbHG7a677psFNWXOJLF5Xvwtz6gWTouoNOD69wTWPppWl58aI20jmIIO9SjiQYWBsoFEDaMS3BYvtwHK84oFlSpblFp+j+vgu1hMvTipl2vCtaeJaLoiZaWOEBaTtLZcM3GZhodeT7T3zEia+G0l3w9JZErEkJYBotVihdgbf05w9Dk7g4V6Isc9UNm2BJkQ/hl0NE4ZCtlrKm2ONb7oOr6crYr2dTbGUihFgfJW4JrQMQDNzAywUineBSymG37R4gHghCjA7yhL/nk2+hNZxbYaMJeq9cgUdTAGxVuIZEkeM/oyiJTMgJtOhxe2ylJGNoXg1scaMdUKkcFFP2K0KzJV5tUnBid4GC+vroNGCYUgd3OKn1CRv9lxfw4Ud6DHevoccQ/unh0riNAkKuVbObSEcw0Upani1ktaJHLlRxr47fx2VwwIdG9hIfcEaPQ3joNPNn5ZiOQoTTCYJVV59YsqU1SUlFJYtrpLZj07MMMPNiSfpmY0bOzW6JoyGm+b5ze6c+KII7nR0ZicILt2j9UI4e76YeV3Wa+p2RqWw2xwNzN7TaBbbC7YPTBhgP74QxZ4t3/5atQnhGLTEUYYfu3kGy4mjhpv3fJeRQSWS/m6UB34zeoQGtMafIkoEsZra+onH75ovVvs6NF2hsLRYhMVyrsCkhtsWNJlE8G0chMyrqwiVipMtY3ihyTKGNGixIsLSBIfkWOS8UaiZimouAEWC0BZ4jq1ae1XMqD2GZpxmXmv8Q+2ZBgOqedhemJA7OJWGsOLd977Fn/z7X3K6u+Dn738L/XvDtK8HdqtvnBPmzjCx4SoYWwG+rFNrL92V83IKqm3MIyiLqAeB/W5l/zkhvuH5Os3TC877DENPmEWSJhmmUrAUntO14XvtHXgdXjksHpfLIfW6M5HeYRctaYJ9EPlW+4Ztq1W0sqvCSoXmubwijVaEWcW+4+zbIWM4++JYopuIJnj6x2JZIa0R9lAMHlhpVSyuL0YpPpOSWlK02CJKBFnxccNMA4XCjv/+h/87b/31ws8vdsjploeNLGq17xSU2TSCl10abq3+rv0G5dEcxu+Xfx568voXHcPRlOKcPML+ML+mywvaIgDsbBuWyrVfHUwpSmaPzJBJ+GyoWlpwL+V3wHVF77V1diti9XSQZitqUiO/qsWYIcwgeCxPwpS1jNQvjhLExVORLFtqbuoAx1auyj2UkThES6Z8YpEq4pE7Ycl0lxFJg2D+uzSl6NRLggiTZqYq9mNqzYKkv/riHzi6fp2n5xecnZ3ll/pyD4nzjgiJTjYL+bRM2yVzfMkC7jsgpkEDOwZek7hvcBReY2IJBtzHgLr3Gw517C5pLemtiVstvqq1wdcTMhmqNdyLtYn6DugiLsWRII5qSZDaoIW/aFKTTbp+06TdHDYxnyiYoiA9+RxWJhfAyptoxlhtqF9DMszpNBy0XFFvok6xxfnViUtfLCyonFqhNLQJi85s5wvKUmmtoux8YWM5FcOhguqCyOKepg5jEWcCv1/2MaFIH0dcIbTqwWJxKYltZKP9M+c1MPHIuGmuB1Me9Xx7QXOMJom5DRD6mIsV634dmK6FySTGqlnvb53/1UCmGOM137repM5Br5Lb0Cw+EUPxQlMLTTQnvJlr9fbGoam6GPM1bdiukS4MVX3NPEruC2OQV/pY9phQ3VS7+RBBtAzZUdPGEJ9biMPqHQU45tb9P6HsLnj4yV8jTXgxv0ycY1rP5w8uiF0L25JVw4Gy0He9wmN2+4UI42VOjqAt2607m7xWKtVhUU/5CTTtwhR3XvKSQ2sGTkyTHABHDJxUPXzXpitVmUSg0Ba1eBjF8Zjvz9HExG2KShNccrEaOjOb6mFj2+sNN4NzmEafc3ZshflpFjEPbWSaL5pfBlwiXVPgi74XVnEGbGl+wMBBtbhj08G0R12gE1XN8VmjXF9PvHj5C55r5WRzwtzg2e7M/XBHoi00VnVM5pFBMWOrjpNKBClirCVw5yX8lozQnQVjhOzm6GZdX2ei+LkZNE7z6VPb04phm4g19KFEuCccL9EOfw65rtT3auksJ0Ok24qHBNQrUIwGxCY6ESZQi9bRGaGaHhPbMCGi5uEphteb2QbVsMmu5t3dl0JLzeOAdtBquMdckNRgWcop0ehS8l8sQQL3YL4wQf73cy18/OIVJ6ubnCM8PHvK09MX1jshDWXOeVomovnCdq1jIQ91QahoCY3nL8FCKV9WpRs4zp4bmEyIZH/q68FEJaZz5jEnLOi//2wBYkeCHtsLk9thQRa1HnhdgemsKsS2cC9QGi37D3uPfHQrhdaJiH+RigVqvdS9gDLZXrbury+UoXrY6tGqp32aM/2iFnKJ/CpafEdMTcynDoDVc561IybTClKQ5rsFh3bFco3aLPzQfCEivBPuW6THVA08n8/KuUDTlU0qGawSEcjQvoOnEQRAijtfvlWY+CJnZ9cltP8mJ0NVbZ86VZbWrFcDCIAyPqQXAWiHFPGcZNtgd/t7WAR0YLi8U0b4ftB1hZ04fXDScVoRWMKRFBvqpX5paL4tllqguDB2UHjjsER2RonEcVRzaOAiDEBHmi2wm0KWVGUPhroGxNNubqqq4vgT00bqGk5qmjIZHJZGsULU5mdaqDVhL7qY2dRrvNytOce3haU4w/kew5mvUUhPf9z4Gx8rqFoVT5pmdwrE6+KHbGOOM4SgF4V67hTd025htkekuhfiGB0NVwCp6TS2DDFcV2IbuD1ZynjCQdcVgsNuJjwJjFiwsGgskuMUCQy30NSqKYRGY7HFjayAQGsh9LZYRdU9MKsbs12dhuSLmlmomKYRD1w2f4YlCz1/6osZlSeGeHuTNRRP+tvW/KYhhdinJKtbGlRuQLmNtIm33vs+jx5+wVzg4vv/FY9//JfM83OQDU0vQM6dwcxNUMjSIVvbwE8xjkjdLR7sdkwX4Y9gyry7M3JgMHGBseZ+6WElCQ0UjM2ld8ejgsIR3zNGbiHRIZDxwEFzq+fQvx7zWiIhTy6qesjB7LomA1lUvuQA7edlKLlyzYNH+kOuRJBogi2lL5bNuXuvWVYTGsmYqqf5PZeK5MY+zTv44zuWerLpW7B0IpLYUFikILqm1HeR+3+K/u73uP6q8cXxK25/+D0uivDTbx3xO6s/4+mLiRuzcvarH7I6+ykbecnu7Meo7FxpeVBXA5E2VGZnpo67xKWq1w1aSKQ0mEWYtNFKtGl2DReax5jLTaQOPSt4vSODZe99AmlKk9kIwJHLxSU2JdN4rrm/li39S4Q3pIC2rMooPuHcQMYJ0lVyn5rLhn9WUppiuIG5TP33QG7gtAwGj4RwSY8wSdA/g8T0/wbWNMk0z1uw3lYlOqYKsqwosmJb3uPDj/47/ur73+b68oTjn3/K7R99zPn1d7hx/gX62VtMdwrbz/6OF//1n3L60UfMp3/I2/9wxvmP/z3T/DMKjylsIVBa1MwxmdZKPOIwQr3qunn6X0LAYsFMUOMoUPXmi/Boe4LeIUhqrlgHE8qI/9n8u+H1Yu7Ec7CvGV8zzeBRr8NV3dUwnXi4w1VPcyl0NjNzCklIR3m5nb75qRWo2W9ht2siUQ1ihbmmS2ho04zyq9cJi3aHYOkQtyg9+CumbeKs1qLCUgLrQNHqsTjva6VyVE948C/epT1cOPpszU+n+7y/ucvH377G73/yHrp6QfvlC9rL96h//ZBvP/0JZx9+h/n9t3h699+w3hWmH/4v3HrxVyivyNIGsUhlzKELUtdcS3HTqsJSzCFoZeicVejHQ7lj76X8sTsUYsUWHl3Me6NBp1v7zlQprJm6hI4ZMecx7+na8WtpzHGH1dV7lClblN3CvdVSZR+tRCkAACAASURBVE3NRIm3ILZKdKQb00bFh5mA2BC7x6s865FBSK8A8ch9y3wfppXUnuPT9w60RuwLF8QqA7HBMh4G0ivCiojDqVTmMqHLGqlrXn14BDpDO6Z9/4jNS+GkrVm/v+X5/3XGxfoDXv3OxPmvnvHO6vc5fyLcf/SQaf0rHv2L/5Lty/+Wsx+ds2l/jejsi281dsL+QlnVS88pB1yw9F5oa0voWyPUkOKKuJ16T7HTOnaj6jqvhz6CNmY2NMcQ9B8/67cPpinxeyf1IdfhCX9vWkaL5SiLJ+tlytor0UJz5lTEiWNJ7eYja677zZNkYCyr5gimKFI8ODkSKlPWBG5Ls4CbotY918CXkR2gK1Qq1TbvUTffijGemgBRJorC1Bae/zcbpmeNB7eFd/525tEkfOtFQf7H3+fWL7f87Y2J9Z+9x8PHF3yuhfnRK9qvzzn5j7/g8T/7iNMX/5zNLz5GyxbRKIDELULrgWwXPEsTpj9PBC2zRDx0l0B1iLDYVFLDESkyFrJtU8m6RpPE9pqGem0XhRwDe+OLv0cIqew/5iuvqyX8IzgsYMl6aK03AatjgqiAjE0RrRxKvYBdvBDUZKR6SKCFhHuqyL4ThZVdJxixJSWZQcVHGKdhfQFNexVxxAslFlfUS8pdmKqaN62FwopFJ2YtrMpCXZSjuXHzeWXzixl9pbw8vWD9SeXibmP9H7/gre/d5Ox2Zb61YtoWfvGv77Ha3aKqsrl7zPLxRNUVRS8MYng8T0LT5OV5GGlOz+6dUsyh6NkVzXBTlE4tpeddTdhCgztqVQstLdKzIcMTBzw8jGtkxDJsrBNMiL6G877qumLCX2gsXtsQ/aa9EABcPbcYrgacolHcTGjfJljIYsMwn/3ox+K5VVdR7APWsXgAZzhxgBw4Lp0HjSJNErNlj6gqygprJLb7Fvdg57ZDz7e888mK1SfnXDvb8PJRRd7eMf99ZXNXefgMTl4K7fMdx5+c8c52x/E71ykLTE+EZ98p3L17E0lG6ELqOp1Qw71v1ehacp/gJR2FEhhVrb5PhxiZNHIvlkUUmqUXrXjC3rIINJWEG1Yd5EXnEqZU9xgtIPcQ6svxhG39WkImaklWIvdoXmc4DqGVzGYW6UWZBHNFJk86pK3apSTLtjUwiH05Y4JpYPHNXRSVlvnZlFR/qa2N47qOiI0xnUGtxm3CSowUOx600rDmIZmVW180nnwHrj2r/MFm4fwlvNpV6klh9XsL069XvLVe8+mRML+z4+Jl4/avldt/0zj7QWGzKO04gs5WaCWxU7qPO9FndHMhnqWZfccomNwyTOoVuxKpKK/M1mCKAByyn8zXDlKKCKX145sNHvv6qRVyFLFyfkVT0Ft4aJqcR8h83xvvN19X2kBHAFlsMsYVxoB5hKUYjmuB/dxsoo0qVhhpgy/ukltFasFM6n7dl1cIE4eTuIeqVmgQGqz7bxBNw5NGKVInRO+NAJHi2qxrTdGJ0lYswZitorrmVJTjLTw9WfNoo3z3ox3nbU252di+P7F9d2H6P58znRaunx/zC13z6o+U0w9W1J8p+t4F83zheFf6Ab02KjelDPPBza+FU4r2boqADx5osyhCwAsC+tkaVA1NaoI8uwY1jVo8Yxfa36kgAT0GM6u91iUt6BhKyS3kDuWkK8XprMOruAaJcvQ0k1nJoRTisA8LGpp5NUCcvpN4OZM1ceYS4M8izZCbHw/XRJeYuInJ75bC3HpRggfuiO0qstTIg8OxdX9SPcdr9W8iC9pg3i5sngk3b86cPoUnR3B3UspN5b2/L9y8XdGPbnDxqtKOZ9qNFfODHVtV3rs2o3Nl9eiJOUlqPa/K4shjcJFkEDiZ6cW4Sx+rrQSpydW0PQOkCXqb9gJpDS2eAsTq3qoDfzO7nuzvJDMrEOm1MOv+nVlGUb7keBx4Xam0CaEfjusRxN6QI9mi17y7S+ib7qi6GdHC7JH2ijMeasFQEU+BpbOUqaHQCiG5YdY17QrUUmgau3NGX6t9s/hWWNmEUmYzp1q8ZN0KAfCq4EJhkZnj5xes323I44V2Y8Uf/Z5yS7ccseFv7y18ti48/p5wb1V462fKt+40Lu4q+gE8vy2cnSrXz85QGlU169KiL7c340jH7bHHlYd+rElJiY4scW2ngpV7ob1+UcPBMictdsKKHhM7mxYHlZFJcOENxo+/D5Ynq7evzmOvXVcyr3kOaaiIgocAgKK2dWiA+CHXHRIYqf5Q/RYetXNCl9KLLUvWknUtkK9B0hxYrKpm3R4aJUweWPFnRPakZy5cUxa8LMogQNF+OImZsplbj7acX4P2Srhxv/JXdcXTh0r7Qnh1T3j8EN590fjp2YoPTxqPPm6834S6mlndXrF6MiMXs4tM7LYS2R2zDHbCoaRACEqVls02UkaHI6jgFSxuC6un2ez/WrdCbklmN9cGth3vSeyeFZEDp7k7NCGkgagXdEhz6rAp0NWuKxxeYothe6fY4G03rOLtnQHeI3DbzVjPL9rQDVQXT6YzHAUQ97BH0OLE02SaVAlpDtLN0B5gsT975DzGH41A2rdwRKtX+w3VElJAVzyQFQ/vV9qq8u7jxmcP4OTpjKzWnLyaufl8RX0E5/cXtmt4/lnh7eOFRy8K8nTHshFkewbhkSKG2Tw+l9tW5OFy4fr36pvoMU4M6EwZC277s/T9V/YCtooLo1c+y0LRXr2txRg2wlgRbilubkTcFL8hJPKP3Ynz4G6KONczwgqVaHsrCdIDDE8DcRgYToTsKLOmGbxcyZp2bTelxcjhailIHeY0jM/K3YzAgUVK7ocSHq2U8Xv0rEfgK2I/keDeqAUM5igwL2yPldVW0ElYzwv3fgLPX15w/MOC/GLh5KcL7/9s4fbpzO47MB8JWgr1VDg+m7n56CVIozYfzN76iWMz3CT6WRA6WcikRdWLJfNt+xXDvOKfV1+TYBqVmCfONTbn4iVfUn1/mcxymNZLCyAWIis5UKexP1OcH0Qk/fGrsN+VqkwsVNJ6DZwA0rziVbyELJgMZxVb1H0X3YgVAWEluv4Nv82iaOuBW8GeW4pkBa8RyjTh3qHDjncsJGqlT2M3fTfZxcF3HN4r7LCFWDRKkmbuPT5leSE8mhpyBqdvCR/cn7n47obNRvnxbeEH/0fhnW/D79XG9z7fcXS844PnG/6iNK69aKC7bh41rFho517PrJjWiq5IoVmnlQvXyKsinkeODATNsV+PuRW3JD1gD0L13eO911a7wKEWvLdNu8MhEeZiI6hKthzgo+r9ga9rwi+7Do/TuZSKlAT6Jn0dCJvarCY1dGMQ9xqJJaU6vMzm5Z1BgzAQkk4E7tWRWYr+hrEjIS7bkjZifD1wHGGZ0gmNmCkNnKSBG21up7uFV6Lcug/XrsPp54XPFuEnP4HvHSmnd2D5QHi4KB99oPzJ9+HdX+9oFzPTyw2/avBKt+6ku94ezFieFCluznwcRUFK7IciOTnxextYalLDtPZq4UJEC0jI0gPPpLbCmaphDIcrE/OWNbGz0DMekQMeY3VXNbOHZyRqZVlabuceXpSUgi6ak5MS9VwaKDQBrcXDsK56YitYyPY2XHWniQhtaVdnGOsX7QcGdy25v20EAycKuY0EEZy20w61DcHNgE8OMuftlt0xbBTaifJv3yr8wVszH9XKkydn/PF6xds/mPnl9Q3fvhD+p+eNP3i1YnVvzf2nW/7m1cy03ZIhIPdYRSoihiRjPzzxTEG1T7M6x+g4FjEYq2RKUkP4dNDcSTUGCiCYMYpdq7yJznfJ914XKXZYi0BpxT3kJbV09C7HB73P9rDrCuaVLLBMbNZcrQ/nClhgVYe+BA/qZsTRTIOFW1ybafQxxIbLAlI9rzsQLTVSSSelP9QMVCS3Y4HiZOrQoGGJxYPXti2GODwwHFX9fIwthen8jLtPFo7WE9+Rxrenmcdnwo+2C9ODiZ+eFj58UnjyTHlwX/jsnTX/8uiMm5vGX16vtJ+eIcuOOCQk8FYXpghoZ+5wMLh4mGXJWGhHz0Fvo2063RpMLe6gyVAU4K5VcJ4fhx3hEAtPKaUtHkqxd3h6PR1EwZWKdoHXK3DdFTbQccksQ12+g8sW+dQIa7grnWTR8CNj5Bp9KaCdOQPrgDWoMIDi2HmouXdXwmsmJK1QvI0HERaJEIw9v6pkuVSeZSO90sXKwvtWN1b2Xpgutrxz2njWhB+dCj/+sVI/XfNsWrj1uHB6Y+HzRxOf7OD66cT8H+DPP1jzs48q136nIXWbccVwuEQWhsBOsp9HMxwrkyA9l9QxdLl05lh69bgSGAO+kNg5tgRTlFpsV4VdWGX//l6GxMeTJen0cLbiO+areoLga8B00YIYZqrHcRI4YN6PBYajGDDVb7JVaCIczEatm2cxUGpIX7EMgRK4hAy3iNfs+ei6ptMeQA1sZi6/3V58A8ZwaMKeplEWe682z6TMOzZPoH2y8HBTuXbeuHlHuditWH53YftsYv3OwoulUt9XLlQ4WwnyvPHwZ3Dr7IJrdC1mJWArp6npNAnmL4EpzQosEnnYqDJeHJc5zg0m8KKJIYtL1C7ONJZiwXED/naVJlSvZSzaQ04LtlVvFA4kTvHgenUG79jOxnqVhP/BIRPDUbF1qUXx7VwFcrt/woyKeCQ9DIkk7rC52WbXE45JiibzJchNMyvD3L2NJh2uLgAJxkQSr5TAN5gD1Iqn8kpkNJqblcBbvUzIKpIbS1NezAuv7sHmYubeI2ivGjd+3jhfhLsP4M5m4eIj4eIYnjc4qWfszhfrD3v0IgPogV9BXFv5zi9u+mvrKTybYuDX6ModaKqdufzbPs+RFl6iqrb1RpEId8Wm4ZVJq4eQ7NlVSp75IfSqnlbMK24iuRFmLDmpkQ/lpAMvcbbucVmLSBePi4mI79vroczgL/+k5PByOsYSA+OE52gsYcalx+r6P3tnjMsWoSRzahI+TKs6shZK7rEnOTIJf8fHGZvy9H31Tl5tEYTT9wubTePidOF6KVx8suPWFwvnny68/b82bn28Y43y6O1jtn+8Qm+s0aWBeOeZOHMMwtlBv8fc8BiZVKoOzTEZJl4RO1P1dXbi+Rz3lIN0z91AUphNqx6pHruLXecnj+dN/t3QhAWrdMnaObF1SsE/lJG4knntmqW4p5Oat4Isbs6K4SL1Wnrxmqs0hBFIdhAaBwOngGYpk1LbjCZzAh6bGj1dVWdw7dqyI5jQHHZ/nHaTpj81Q0tmCPhsjNlo0lh2jYvSuPkILm42Xv3+huNHyofra/ybD7Z89s4Rn/984fQHR9z9T41rouinC6fvFo7rzgLgGosT4Y2aWNe4LrYNGwQhtHjiJUtlBehPh9sVgu7RuWPvMWRiOFq7kDrdouo4mg/inuZCvPgQVi2gkaXPDCqNtT6/+boypjOp8GoTbYY7fKaK2XrbPb15YaabLu2bJkbkQxSmqM3zMaeJyG31vQNeitflWQWsaOzSFF+MMs3QqBFAcIFphapWydaJbHcWHFOpnTdR1PbO2zGBNNZzYXmvsn3RuD0r7eeNuxcz37275rvvXvCgbHi+hX+1XZj/aObiQvlkqezOdhw9vciSLtFggJYRgGjlq0RWORwl6bzm1B3PQAsfN4o6Lbziheh+nzgjXV5Hg64RL23+fs2QTWz1H9Ulc7FohKh5surrOUJqvj6mc3xRZiecpW0WjbMlNAkcCz5+v+dgTcLVAWr1cpkWW8h7waHt0BSJe3tqnGATAd+S75JkuqicUAkm6x5XmPdgRhMAgSw7ilSQV6q0ynQBN1/OICt0apwsO8qDSjt/yU8/FR7cBs4LP97C47uVaVt5elfZPIXp5dY1XeCjycagu9TwxRt1jIl6jTPQ5ygNLT0UntA/whbsOwojc497Fquq1yO6hhpibBqaFFvL0KiTSXvPQrmSiBBLWqoDr8MdCY+pmRQV8qA4t/uIeF6weRlONw9h86OSIjJ2RcUI4HxUiu17V8UArWGrXmpeMNOuQ6Chh1lCSwzhBf+PaEncGUw3PrMGmE50bF+OQ5PlfGGncHQ6s3q6Y32jcPQtqN9eU6819C3l7aWxLcLtbePVNFMrbG82Wlkcm9kzzeky5qph/sUtghFw+BclZc6sugKdPG9s92agNvCXaNIicHLo/1JKPxsDJQpFtbhDVaL90p4ZZWzBYOmU0XF15Luvcl0hTmdxrG5mu/T0voVBfXtxYVe7XX6DR4whpPfA9spFr81TtNix5FVtUYyh+rajcWJ1lFv1A3l9nG521DuwRCR7dcHLdQKXhKnRwkxBZUFaoZxdoNcKy3vKcmvN8+fwSgt//mnhD5/N/M2DidvbM/7+f5u4eaS0D2emDxe4WFid7YhtEE1HLWRFjuOlGkzgVAyqhY5OwCBmXaKEi9iMMGAhMrwnNKcBiLGcPNdp+F2d/gVxDeYr5+Y/HIjY66SfmmTrUTmc8a5gXnv0P7QdbjBF8M2qBZIIPZfKMMkiUYulxFkLGtgO7RvcGA0JUI12b5cQAMikvykp97icgBmLk1g2yc8tjF6wrbxiievgaGB7I0tls4W3H8PZiwXmwu2lMR/P6LXKq63w9D2hPbjG2XcEeQHn05rNE2X9XNm8vHBnp3uTYSESEAxFqeK52KR7lIP7nPYXpaWjpJHDjWMEfPbmYTqGI8ypEidOxjh6btbfEcFlGY6h14h9mvrUqOLR4ITDrqtt6R+ep/V2+SgWmgcOvZ7EJjJIcmw1oWqmLMLEKT2kNbG9bb2HQhDftqv01jo/0YaAItqxjE27S6Y914o3q8DSYqHNk7HQQrPqZQlvJg25+c3SODrd8U/nC/7qfM2jR6/48CcrTrfC9U3jxalw8x+2HG8LN/9eab8LqzvK43dhvduZpi5m7nKPTxHPyEhmXKrH4zqEkaRJ/GzcGwFlx7pdyXcF4PMz/Ob09/eYwIsHeIszTJhbF3zwQtt4kh/IHJpZrSoo1xVSsP+zMl0wWwfahsfK4hgiDhtGM9jrPOK7eI4DC7aoWZmj7lFVhCVCIEqmroBMcUXIQ0JrqZ9sIKBazcOKE5jDPMR7nEA2VutwK01QrZ6xCAOn+b5Hk2UiptML6oM1HMOym2CaWS4KtSwcqXBxY8fNj1esru+4+XjivCgzW1bMndHBRMQD0CWgf4dxg+nTAX06OPOmwtR/kU/1+3sHXCOOQ40wdDoNYX324i72f1WsgLM7IPbnilcbR4mRf694evFr6QbDQaVJjrvjNCsI9AmId/Y3ujQ1NHFf2zNtQVTpFcZFWAyy9M2aowSKCI9ImuQIfMYCWRXyQm12z8J+9Qoi3r4YnrSn2aSHKtJhUrwzTNlNjcerNdvjmeW7le2pstwX2vPC8W34+Mmau8vM6dNjPnjrlOfnjbefwdNPGptdS1wVNLTwTiN2OrKjPlsvjdNOb9zZ8uETWs6+5x6pDF6khi9v1+L4LNjd1i20uuPgfKZ57FYHsJ+6Q/EuO++FcR5oUcZ2hTzYwd5rLcVSJMVaAEsNT8jNFBUplUiUh4SU+D20o2T400CxdMnJTViFJJwFo/HY3JKGVJxBci8Up1CU+0RuMJlSgrj2Oqv5KNjJjNXH1j3IDOACtx4om03l8e9XlvUF118slO059x4q7eyc279sbKjod5RdWfHorSOe3ilMu3OKeqN0eoWmHariXr46oysSlawyBJwkcLSbVvFKGqmoVPCzWWuYYzejDl99ruJaPAF20tYtfad9qkKvnVMSe0bFeKxJPAuxbNSh1xVKmyazlS2O1uz4SPy4TYt5eWS8RSN2r3YQ79Z1hz61TfhKzbvL4rwKDcohkOVTXbvZnsCFjBNp4BaSEVMLUC1ZLX3znJaYpRHIsFqG0d5TlKnBebvg/tnCbhE2Lws3WuOzhxM3jxqfvrjG3Xd2tIvKt3+mHN1dePvTiWd3GsuRUmSmLE4XsV0LkNnMvmu4KIgwRoggsHpIIgS4mzoLh5j3rgni1cMlAR0YaGvMEfucWAkYpCpzLFeGVEeROmDufZyMFwXgdOw4+bDrYKabXCoWAWphWcyEhvKOwk4VyXPPw0NSUd+ZXRKaFLDeSqKkZwip0AmiwdT4/sMKRFVrLEikt4DoUmrOYGmzWl+G/rEisqBeWzf2noqYR70VeLpp3CvK+gE8qRPyrcYXd1bce7RwsZvYNKFstmirrF4WVnOjvhSO/2EGZnckwpRG3jQ3qyWmGw5E7Gpl+LVbBHV2kggdxU7wTr99qjk0KSVTkvYndXPt73GCxPdEokwiSpb8Hk/yx1Yi4xHtEP3IB/LSoTcWJpoUpDSk2T6XCJQCqnbyDa1ZG2I3gsNWBZGAjxyg0IWjR9IryqLBR17ZELVnLtl0eUPdrzJiDSSX/vyOXjzllVpW8rvifzWoEGDGtN+dZzNHr+B5Fd7dKWVd+PbRzJ3b52yvTZQvJuZbwq1/Aqsn8OzuxO1fbtm9eJkJdvBGGW2uvarRMsrBHGZkgp7IIC+xiVkvS0KIk36yHMz/1mg9J+qB8ky3BWNqCJ8LuptkJfK1nlsXIexEYGIJTId2xou44cG8dOiNxSo0rINoiGyLuMaLKzhpAVkcp0SkXYeCw56A7hGrjrfi+ErbQtEPA5DY5agxguXipUzR4WTfUd+VxExD5DLTE5Ze/SKOIwtWqdsj7YZRl6XBUnl+t3L9XYXrwsV95fky8XglbE4Xjk8L7c8r8+PGRpXlXWW+MRMnnRWUKgu1uIA67eJ/UdMmYQZKAjRjRqKSRvIMiP0skVkExHbTqjWwNK7JggbDWsjip5V3FBPi3HGlZGpOhveHFlbpGYtDrytsiuhPLmKdXaJ+XFFM2JikOVgVSAwG5D65+F8Di5gW6lonHKuqFmZZBqww9oQGfpPUmIls8+7ROyYJ6KQMLzneHwwQJltDAAov1sccv9N464lw73Hj/iKcflaQc7j568LJvOPVyZY/PFZ2v17Bi4Wf319z9Mp86Gh4bkSaqmv78CYjRRhz6fjNA+hpO3DtpDn34r9HlkaxMu5CNNrgNXC6Rx7T7KH1vBC09O7f9KQZvNvc3szpb6o0vdhDrittFdbyfE+bSexRNi66iPqZoP6544bI23lwghg24QEkmusFlfEeS1s5Y3gx6OIZidpc+nBz4jVlaPjDOjBRh7sZZQfvVBkRVgRxQVhY7Z5z59ewPmrIifLDNcwP4MaHymd3J06f7Hjx/hG7Xy1s5sJ80rj/dzA/egEioadxP9IcCTqeAjJ/mprZXc/Q+MYzof0iOaZ7mrq59m6EyQyh85SlU9eO/xoL4UPNdUHtIZVwTkKZjBAmlnGESr/5ukIRpxX5lagJckYLonTXuiCtJAhVGcIb7FkMYwEP3mYS2//WWdCT5a7GCwtVlChUxJPUmqa6YVVhgZNiHw7Fts13c19qkt0qcmuOM1WO46y6XdhczKxPdhzdFyYuuHveOG5w94c7bp3DzY+VelN49+aOD9+CZx9tWTbVm1oiT1xJKokH1yXm6FowCOTWYzSPFuLT/FsJTSm2V7K0cCFiF7yoPPZAiBdXSBlCHFk1bCAmKrfDzGcYJvCg48AymOwSpv/A63BHojY/ftGOW2yqdrqL+iasHi8LZaUeLBbBj3Tq2cAIF0Qi2rS+pckgZLCXS0U+1jautgXpHqymUyJBRLw7PUyNcZM5KBQ0Bwqj7CL4trPVmFotpraVwic3lQ/mDfeO4f4PVvzy+ZazWyveWpTrG1jfOIdHcHIH/uLRxNnNiWm1Ze0axI6kWkAmhwlWztRgWLDQX05DRsw22IeMs5nI4LNR14ymKe37S0AYHYQ5NL9IFkREDwpSab7DQux0KsLQQ9wtQgw7gseHXofH6VxFi5Ss7VLwqr4lqDGkrdwzcoYK9GQr2bvTxf9r39CBiJqR7whyGmEKaE8n9aZik+rgJdHo1jcGNfwkXqunffHEu9ZtYxbGZuwwLqXB75wq51X59fPG+Xbi7FR4KoXTRZgnWMqG+7uX3K1r7mzP2P1kQp9sWVwI7P8DRmCaVSEO2DMhdFMaWk26po9Evd1r0jyeglMH/GtOV18D26ZfsxrHavBsznbclD0nv+OHOLtKs+iEWOf/GJNLPhug4iHX4XE6i4iYpqmgcyPOhaGIH9DrKMAJZKckxgG46jw3JONxaBzFk/l9yGROzmYMl/ii4KED7ZhjPDgtDlPLTrJBw+WW9cnQBVpkQ73zSo8QCtPSWH2+8HgjvHq0IJ8tLM8EKVvaFyvK+1v+yb8Snv5k4odfVH51+xpHcsbSlMkXOSBCbz9syfQaGYmBUXKkzmCBVzvocGynhlyjzFx0aKsUyY1wesGoXSV8e6d9nEdBOCaB+IQeA3SnAiQVxTiig3np0BtLKdRqarZpZSoNbQsz4wkrca61Hxk0nMoSV+QII5jbPBuQWYQRUwxaO5p7gykTbPs9UXIT+Uow7BJOVeydUuI5bnKl2U7rgnmAs+ceJzXmm12L7laN9bWZVyps7yprqbz4qXC7NV78Wvjkf1Ze1crFtQXdCGfvrbm2Xljm0CKLz3NyrNiItFZuGCSh4bTTQCTdkJhH39nKsVZzVhWQ1hiBuhA4LnbPcpCjvj8gajsyqUTvtcOfEFR7zzLgOpcGx+Nk2vpgXrrCvUksyxN6jC4UrgYmK11KIiCsHa8FT4ympGgnZk5Me/ugYlgrDk0JuBwpox6fkoB7rgmHWB4RzIx35EBcKzSv6G3AYhv4eIoNEVpbcaSC3oGbG3j7RFnehnoLXkjh+vEFnDXa2cy6bbnxqwbnEDvMh7aKjfuc5Z3JesQw7o0ThyTqCIfqY5IC9iR1rzeqkFNTurOSDBnaUALvte6w1dDE/gzt34/PC17Rrf35uahfhyOBGDaIozCXYJACtIVgB1A7owGsULMFLtM0j1GXZYHz2CJCEhuEPIrX7UW9f2gyxObhRAAAIABJREFUQY0pnLp53FIQAPXIfEkHBDfjFqn34I0UP7i4n5JtZm6FyJSYqbFw8xmcMfPuRaVdVLQ2jh4LJzeVW7cLL65PlK3wcgWrB7B5fs6uzUYtsQP20suTWKcIZwxkljBrIUiazBjB2UC06rispEXpJjrXw81lmJDmUhn7/fVXB+wR/5vuYbU4WmCvWDPNkN9/4HWFyuGo0OjplFKEohGGCPNoWMXSWb4LeGgoCXXtxwY5M0WntmE03avNCmZSAu1olvMEw5F/g8hTNg3kGMZJev1d08Q0eZSAD7EyobqiMtG8FkUQ5k1j+3njxdNGOYdX24mTsnD+FNbzwvH5OaWuuXMHnj1RuGWQhAVq7FNSwgzKMF7X1PRxlNTxvtAV318utLLddznfuVeCrt2SJJQN4cZ2Q7Up953wY+NriLXsXBWHIUZUhxB+upI49DrcvGbeMzCUe0IiiJfYmKSZlyqej6ta8iXREdanIlmHV5qVLhWxpTYMRvpLI+OEce6Oh2ncQgO1apeixYszyX9hFggPO7RONEB73exYrCqiLLVxdn3m4myiyUSRyvVrK86XynoSPvli4vysMm8b6/PGdCzI5xPgG+cMQdeIa9r8m28gLdQypJtKX1izMJIwqsfL+t5ze6ZuuKLubmwXlFijHoAbmDHhMIIMTTzQxaS/y6DR1eN0V8J0YRIu45De/9BRh/hku39D1r4JHigNR0j8II5i3lQIfWi2YOA0pxJpN0lmEbU9UFYq2BnTEUCN4DR7IYiBgmGs6PCrA/2iJhA3HgkfvF+o13ds1jtuTue8dWPmrfdmjt8GWdborvDs7kSZJ6ZXF9CEicXfUU3AfIF7QDhy1yG06Zv6YaCSyXyS4VxteXHsZTzcd1sY6DiEbhLeOCa2XTfplkgG/CjkjqaB8TKPHfwAVyrivFLfq13mc0ayOWghAdJ8Pduo0sUH5ThBidBG99JG9WzWeKhc8ACxODbr1V0NtKYGUfeuwxuOwYVpts/8MDgtxMElpoUM/y0jg4o5QE1XLLpw4xl8p61ZTgpffLYgTyae7F5x57Rw+/7Mrqw5eiI8XS/s3lPqTwDP4tjCq9UlYjlZ2dMfOfO9nyW8WvoCJ9HJyIXRcKgkGddtv+nGGUYNfVuapyX2Ve27PGWISby4PwPEke/1JW769Wg680DDTJWMjxGl3p5+skWd98iXiFZd07iUBbGsuiO0gJsiwXC/M3nx5m5cE9QAyM6udsrN4IQ4bioa3fMmAZEu6+bF6sOa5ZBMGyhEO6JKo0lhdXfhzPJwHN+ARmWSxoPPj7l4Ljx/uMByzvrFOeVkYfNUEN85PnCUFKGoeeFpEv3nqFOOWFuN2SmgbuZeA09jfC+s0Ov/SomffTRaECZEauq+ombeKdEkFNbA8thZEdh9ljyELszwodcVMhKDHMYROUmAXgaDeoFfJvodbHpSXX0/ksQ4KsmyqdEliB21gyWziYgxglHBuy1DEkOtRsEosj9O31QRBGm2zKLqab2amQ/bTMYku4oibUf59YQcbVkeC22lXNtUpruFD+43Hj6C69fWPJu3tAo3Hp/z+ec7RGZ6ASb2bu/jDW3aw6pdCE3BLN58XlJYk2aEYIWWjmWweY8nD9nHxpxZ+RuQxIckl4ketwj4Fvr5PB04L9JfMrzzkOvwkIlqrypJu7n44hf/e6/7DS5N7eiRx9FrK850oeEyrTdIUwITf+Qo7JG5ME1hEtuAKpWm7hZIksfNtu1vbGmvWARzjYpOLNEn4Rq5echnV4TVC+ViWzh/1Lh4IOiZUjdwvAjHNxontwpbnXhR4do7z3jxwMvRA8eV7sBkOWkGhUPDO0OWjkVNGwvZ05vmlrSvyTjDf5NBQsMlxJDMuUaJpqiXw8Y7tH8eb+iH+IEUb+Buw/sPvK7gSJiCTXkZKvcsjdIJma4S3bu0/wIsVF0yBxrMMEbdBziGEhsxOw+qprHs6qJP2cqaAi9aXE6DufNdZmabRHzRBSgl2gohVTTDOZu1sD6emIpyfHNinlfMy8Rnn1XWFwtnjyvPPxWuyznXtjN6boGhMXXUfPzmfJUcukikxMTn54Wrl5jIxt6oxD7xxrBvciaGr+VamWds2ZE8e4xotfRCVvE2xDDH9qE/S/vjckFs8bQcznaH766eyXB1laspBRFEjR2I7FjyyBrEWPugumzEZ9LzuLqv7MJ0xxb2GUIRcotY01x2dw2iqBmi6qVMNkTpL4i3S0T/i21dbxMyWqq/QxYuvlioHHHtZuPs1SuObwhtU7mzLqyXyjvfFX701xP15JyqK7afTdawLFEE0cuR4t2qPdQQ8c3I9OCaP7fp8IXvZ+heNmddZEez2mvvcMydxtm/JnkAdHy/dSrnSigedwxHzb0Jia3DrmBe/xGOhPYXq7gC7PV0Ta3fNILk6V3ltEZQqxmeiHSYpWkkpTS/FwvgDxVGh8SMxCKweLlQKOLm536V3JnccF0csGY5WwPCNZwbf2vXHtCOLqjrhZMbEyLHlFeFa2cLPFvQBX78/wg3jxqnD6+zPhFu3tsSYZ1wnozYQ93gEOLI5H8uXsTItLcqDgsbpB/dOKsbvKTtsH6MoKVhnthw0v5FfNB2OR3K+D10MhG7c5JCWsVipmmOr3AdznQdgaa/qBEw1oQevpi9Ca67XJFF0D3zOzKXW9HhA5cyjdMMs2WYqJaNXKJ4ebZKtfRXYpMwyXZUZe8zhWjmKX6+mDoDZ1lPVHhQWa9WrDYL50+2bI4bRyfC0xcbtE1MZabOlYvnytv3ZuS8IeXCcFPMSdUKJcXMYu4S5fMhxxWM6Dtb4du1avHfLD9cBrOaP8ca4Kydz1K/L9jHqFii3zcsktOlhmwSghHZqL5GqSvdBEs9HKldISPRIby2RmtKawvDccIxDPtJ41/HY2gwTJdwHB+maR7CCL1xhQ6aRRyPie8GaY/IvYgJt6ETsTN6N+wlAhTOiBamiMVzNZkxvIY+FpYLoczCq49hw46bt5Tjm8q6wHsfLdSq7M4a14ry/PONP35LybNrXTjccTJtXQjHQkK7SaGYvSDiYoEO4sRGm0cvE0hNFYrBiZYVwoLVz4nahtqiXicXj+t4LXT9GOrvmBxfHxkEiKT9Idfhmi622E8zuxgOWRjq1ExTWKwuaeNEdVZz8+yohayirQG5wnwHJmGPadLtN4OK9Y+6QzA8OxbCX2/v136WVTIzIaySnxYpSCme4qlUVpS7hem6cvvdFcfXCzebcO/mGdde7jgR4eEDuH4srDagZeL6davM22vqcsekSeRNhxG4ee3OxaipchVyPSKwbuXikVGQ/G6WlSu+31834fbq1Ap7TkhozBK7R4llior0fYhDV0ZfRwrqgdfBjkTDtmlt6sWc0Xsjpvk0qupkvzYuSOUFJzk0FToeRLwh2L3ZALZO7YKgreXvbUjnqP9uJwZ2rzbYPxY3zIHxcfFGHluusdo56pYXaSAraKZPVqxYtxXtkVIfVrbnlZ8/bKwK1HnhTpvR9QVHR4JuV0SpvLoOwmN2uTgjv/lYzCTovvOBafPEdXSmysuhgs3PBNO63lwRSI+bNom96rrmH52rETtbSGs/Vx4tAr12uwv6odcVzgZTdGksS2NpLQFsBj4UiIYYAtrbLKK0qDhD9mZhzfMdQhshPQBpT4rezajvj2PZu74DfMNscpEy4KzBRqFyorYvvO+ozDXBWSy05gsb1cUL82PhmSxcf7fR5hX1+ILV9Q0rbTyeC5NO7F7OrK9fINvGy2c7YznNAvVcNglB6HJld4j4UWFLahuNz33TnTZs6REr3Y/N8ncoduSSdP3TxI5gIDI7aZlCOThNZD+43DfIlkvzGJzHMizEAdfB5rUtjWVZbIt67erOatlsjw7V6nuYCLWppa5c+9kpip1VVM1972Z2uGR0KHpZqrp5D9xn8cLYYVMZK0QiVpdFp6FgMpUWlnsofSJ0Ys7azjoryvpeY3NjoSAsVZDSWHZbCjPzuXJ8f8tqmTj/4pjNjZmT6zOBP4Uh7hX08DFYjaK/NxyJIp0Gibd8O9e01QO5RmhA9y7Duw9cazG5aIl8k1mVfB6QtA68mKk6NdMaO67HpjqHXgcz3bIstGVBm+dX6afkqXqDBxZPUw2ZiFLrlp5E10/eJ6D78hPJ0f29bDUHa7nJKNVxIxqpiljQXNjoCvVLIDaatldZEaQEdmFCmPxG9xu9crfslPZq4ukvK+trW9ZlzbVlYnU+sWrKZz+Bk42gk1LKilqPh679viA9eO1goHrnlf/PNgWSDrlaWAVABdFoA+1rc9nUApToZnMnxZqZajL/6IREiGSk+eX8beByqx6WN4a2Dr2udPTmWJsWO3X3RuXBkTaUyR7wHTCeUwVp4slv3/PETUcGRtS2lI2gcC/mdEJHkRzGQOIdKVJaat+KEA1PI1M3uueoZsVpLKbBxYTB2hcL6wa7Z8LJ2zPnF7DMhU+fFC5WsFHl2nrNRhtH64Xr9+Hp8y2nTy7sOQjRbmgzm50Yvpgav3bzlgITwd3BDI+KTgM/0xkvwz2RknS8VQeMWDX0ug5eqVE2cC9g5WaxtgPWU402gx4o/lo0XWvxAk+3NSiLZpFJ74MwHGWbV7vtB9N+EviAnGwwUgabL9sOBGusDkkMFNkXqAhMDp4jgS2q1Cj4rKVn7VyrxbPjP6Lk/XGuTNzeZM3mwx3XT65x9/2J4xuwObmgvYIm53z6xczFpvHotPH8c+Hut9Zcu3HhGney58sQuyS0zZKhmyiIjBZwizuqHfVkutb+l7lqy0qUYEKnjVPS0l2OZ/Jcj8iHO+eGF53r4TJcXKDN9O/3ZgS9LKK0b44PvQ7XdLTUbtp6E29riaJAFg+txIJqMpRI5EGDdYG9ZRhaitxUhgRK5DA1srspX4jIUJreIZDoYMa0+rGTkZ0wU7HQPzMaxpZmVnlsiyJWV/dwYruamafKZhLuMnEujbPnx0y6Y3cKbTWzuVZYLmbWFTwpZ2df6ESRLXlaoPhexK7FtDhYEXe0fH8QwQQK7/kQVS/5GrMatj+gle736uxRtAz/Dr8LRDuohnpwzNjPmiCdswilNDSfY5r5UA7q1xVyr82PZop+V59aeEQ48PT/hvhk2ETBSsk9Oh7FAtIP0+1eVIDYmNpgToJlJQxEMFgsQpiqKAGyxayOl+Jk7awHTEhgWMp8xAGWC0wNtqcVebGlvr1jJRuenyvra5XVtYaUytGkrG8V5jPl/PPC88dHqFx4aZZp+diSP/tX1TaaNM/Z1a3gptg2s7UwUq80NqG6rHm6lQjhUVELM2Uesu/lrPSNKkMpBOWTYT1fHfgcdSENM6uO3/ccr8OuK5hXw0bLYn0I4od0/b+0vcuPZVl23vdbe59z7r3xzHdmvau6q7uL/QBJiRRFUTAlgZQsC4IsAQI08cQzAx544JH/Ao8M2IBlGNDAA0nwQKBsw5AgggLNJkVKbHY3u1v9qq53ZeU7MiMyIu7rnL2XB2uvfU5US2Im0HVRUZkZcePcc/Zee61vfeuVpWi6UpugKtjsVbVEyElKlJQPrGVvSs2qcBzjwmIPClPa0ZmvWPDE1KEIPjSFcg9QYoOhRC5K9MEPhgOlcrqDtzRzzSda+scJfbPD7pdPWNAy3G5YN8pyGNg8HRi2G44fKY1kTo7n7M0a1quOvYUW02nCa1cuhT7+0ZNn9miCrYmgPlnaxx7IVLMV0x/GWgcRQYPabLZgHZw0QI5iX7XugtEx8DJST8Is0MOhTAQaN+miNMKEZPZdKRhYnl34nl3o6MmaKhgTF5hsVsKwRtFS6ASfOUWaCp/kQqS2sIyUhsUAp2G1UUjce/LPraCw4KUaz1Up1yimC+qptr5ufqoL9lEztKiiYaCObyykMQpN7snvH7BtMmkbOL03p91t2d1vGOYt+/uZxa7y+EPhfJvY2c20DVU/mZdcplqHPB4jKZ0ExKMoowD5WgiOqybr4F+qFRFWnFiUdyCMnqqM9aq1RsTX9wJtYo5XlNGrrZkvvh7BY73Y/YVRCz/r6/kyh9XN0iRMNXFJg2pp6ToiCk8xcu/TIMg0IdQu4WSymzhX/YhHKS9SH4JXnZfNql7cxY677mkZHnGCeYIqg/U6zoRC35QewBkkB9wR6pMScubgzQjdwN0/iiyCsiLQRKUdhBcbZVgL85h4emqt912ve6GQ39moaItXVvoPh/qzhHlFUpyjkR0wgfPnsPWONc6gvkQj1VK95BG6hAprxrc4WnbKKWfvDF/RXMF5DkRlklv37JruOTCdjl/FW4UxjyoXdt8w2rhZdaMxbViF1ReoLEi4gOkca5UFKX8f++p6DLZ0YJqm/OiYBGAx2cLLjVE0pCBTKAXVUrSjNgRN1oevVKnkoDQpsHdZ0dMZsqvs3xTuDoHlWWIpW7o058l6oL2RaFuhOchc3k0slw1BVtRJQniR7sSER+Nz3FeuE6OLkI6JARMcp2VwnWCxi+IZOOmhkxStYJeqP5NsmNrFq+yerWmBS74PNexWnQ27hoUWQ9nr0s72OYTuOfLp3HvNBlArP+duBWWnFckJLz80E+anuSQR1gUM9QHdWjphS0nXrkF8zYbj6h05daBFaBQPeYhfW2I9/TnaNS2rwlKdwJoTAtQa0QDWxbIchOK9ru+3bBqFIXP2k8wQE91NGIZdGpQX/hycnwoHl9e0EkoakwlzLHFTLS37pZhC0xDWlswPihQBqPQIlEJrz4pxsnZMkrXrhRqFcPOoBU54Xpx/Lws1hmoODmVitePN4g07CKTk5EmDD0s2M5vHrJfPoqW/KjbXPgtRzScc1L0w3JaZ6a3xTTeLuZC/Jrzi4ij+KxMfqCyiXfqiFsMRYf2+YsNHXK+m4vLHeksipVV9UTJJ7N5DCXw7BmL8o9IGFEwl0hJuJsIiIHNI64G2a8n9wO6e0OwNPLrXoll58nDG7HLivA8E+uL29LiP6GaseqrliIzxZ1BJRauXijApmtmdAKy6wbukUy1E8SjzeGDDVMBFKk3iny2uBl11qJtTGXEyHn70VSqCp0K0qqapYfozX89lXqXY7kEs60PU9dwoBx6hUU2jgJRFVffMkCp8/lDIZNZV+YHmETwzfa9/r2Ry+CsQbc4XjLgHismSaiBCwWylqaWdcj8/ongDZ0/LjyGQArTSEOZwfNLQR1iuO9KQaLbQrxPx0sA6teiy4dJOy8mpORGxeKwGY0NxHsZWYR7jrQXk7uGWOWhVYCbL4Lynz2izXYilB7RrqYzPjvDTVQ+jlhqN0p3eQ5plJBIuhNXaVBfEZ9NK4Q3LuvmpfobXM+tEKUF+Vc9rKo2VS7btmJ9eOE+vqlKtm197ieB5YFpMptSMV4+tGjXip64IkT1qPXFT6sE/3E2XkKwLE0XgGdOwp9XrOQxjYbi7/55lrNG+gLMzYdX0nG2W9KsNaKa5JfR7DSepJe0Iy6cdMxJn2w3rrd2cN7bGcZ0MpQ7VEwipLV3tOanRCQuwe/cqP2yKF9VI8X4RHyiTbA0rvRJq/l4xknWNoySCvx/vJFD6CZQwhzf7GTOTTShrXl4Y9/95KJNnL0GcqFBLsTHAGYoXWpnqyganidkKE3MJHj8Y6d1UlrdAaIVqWsqGXTDBuAmc/BsjSA2GTKdhVzeETCqaOZAlVpNfc/2CEIZAkkySwcY3qdC0gb0XezbLyN6NSFpk1qvMyaOelCLdQpCbiq7gPA/sNcpq2CBSsG2pOhvp3bJRPpbJpzC681AIdM/zm+r5umZuHrU4aFqomZAvPLW4VZkQuZUQKXuSfdElEyiVerWblmPzQjmViI2IXLBEz+FHPAe94thLx3y5cRsnmXXlPR620po6BOjF+Q+Z0TyPwNBMnDAdBeTvKIs2EW7Xf6bxHIeN2lAnizbl/HwWQmA8BEYBlGt4CE0Sg0aenkW0ER4uG46YsfeFhrO9GYSGzUZ457uRxaXEeRPom8zebD4+u+fAFZhmEGwMv7nWcEdLgyefTjR8+bOSyBQerYSrJBTtR42lEMrE7mbSYDsSiGqNjWJxVqzK0A7AeFf1xuwQB8PqY4ERJRV+tBrP+nqOdPU08mkTUrhG7F1gJmGXiYdRTaCjsJqdKtOYg443JaOw+IkvkVPqeHQ/9UVj2caYSQ/q5YcFP3nq+ITE9v+bdiwatXhziJHGSiSR2ObA3ssRHaB/AsN6zSpH8mFic7llsQA9jYSgrPPASgd8rLwNftEiFEqYkOKusUawZV8+LMnubyzQcdHxUkQL0DN6qMq4NrW+NZfoEFWA7eCV5ts6oEw4ueC6sJh3Xw/3dDCAasIe8M77z/p6jjFNozPhHmeRCGrDQx2FSbNvq6neir209DzRMYXJsnmLFzLRex53NOBb+KWqxl3fStUa6v8XmQxVCROrL1XTTFLwivcTqqdrDo9nNUe43NBcizydR7o3EuEYZnsg6551hpO84s0vCJvtwLDs2N3ZJ+m6alQjfEvctZwe/7ttaGn/Wg6DYyx7ApcUf2Ypwms378d1qI8Xxg70RfzMJJZquaoU7H9WblD+6b6AOnwZrVJUT/+3/Ra1kJ23hX0enu65iq0ZfRz7s0y0pphSe1/9qT8ZTB62dgJwvOdmUm0J3af1Q+VYUXSyAeo6sFZoTvBHRutMCHGFOcF8pZBPsg1AyU1xhBOopyFl4wop0ZSzyKNz4Ua/ZYsy7MG7u3NOZ0IvidOHwicfR3auJJqorCWxf7Pl7GNMwKoDVTS+5JpNMlnNIvTOD44HuFIYvhBlgWSiJRtfz+LJTpuOVzag7qVrSNuHHKC0wbf3TzxgRet5cCfvokn1BpTP/nquXiZuhQTGEJiM8YELaFIdCOvkEnbDxmJH6iSXC4IKU26gOkV1Ayb/K6un6hvjPm+ol/OZy3YmAkE9hciEMHuvkZDr/dn4qZJClBU5iOxcCTw+C8y2A3tNw74u+bDfR1/asNqds9WB2R40Z8KQEk9OGnKIeBmhmzQ3467HxjoGqUOpsxjBW75bTCdVyKYp+w4PvEOA4vlw1Di1/dO7LhQivUIijz/7XjnjeVG4x6QNu69cMKBVE0jF38/yeo5x6tSNnIJGyVpDLWkic1X91scwPObJl0alOMdXdFrV/K41x33SqYAXW24VT5lcw+EKNd7prQSdHAXnpLxk0jfeH8o2IwEdhIwkSzEKXeB2DBznyPrpwA3Zcv7RAlQ5O4l0pwm9lTlbd2wuARvYvXzK6qykKEnvYKp87jRAP5o7D0x5G9Zw4aBp/R0TBL2oKR0j+7rVa2q9hndTR0zTT1g7uyZizy9Qq9PKRoyH3R2v4jiW9z1P3etzx1496lCfRH2pPkUOippACubhKmOc04nmYjbG3xw5O8dbU+rD/l9OWPndEceBM7wBjw/aBo/zXG0adK7x2lIhJQOSZlBa1abxkxDZsj4VulViv4OoiXOdcftqZLaBs4OWbgXnS6AXrsc1R2FgNrdoCVMOzp+kaCkpeM4PhXv2F4gSofKNKq6p/BoTrTexMsIEkznGdktVxMVSpiYWpsKZkg3ja5pdM4/3k7zgSk0jeoLns76eS9NVM1jwld+cpyxNVIp5N0UQCo9dAafV0IYRyLumqUs2vuSnnkbq+6uQYmEhrzOYaq/6O2LzdSw2bCM1baEtbGYxyATMyuYb4ZqZsXndcNzprcgq7XLQbNkMW+YSaNuB7a0ZD64ocke40giHh3M++jjTBCGW1KULz1q7c8vkOeoRqffmFEVdIQkVAny6B52b1pH7tNc0q8aHFwuQghKzlJBbwWyOm8v++T1+GrGZRrS7Chc/7plezyF07kRMqq/Ut6d4NZVAtifLymjrC74ofzXzUW686oKJzExPaCUwAUTJJalQXBXKRLhQ0x6MxT7e1tRzNuqc0iqXJdMjQB7G7R9iJg4D623gg0bI94VBA0cHC47uQoiJx0+EF7awXCTWIfL+IrLfRboXBR47zrTnlLIuY0LSNBRYNF0RxKAweqmM5jOE8fBPXhPkXDVfnexdhW0EFA7OpexA+TgiY+1EzPWKOEUCni1UrErWmtb/rK9n5+nqCXDsZdKR0ELmmtkL4qno5QRrEdSiGROOaSgaT/3QVErGFMLIzJtXN0GGrv6ZnHR1Y12MgVpRi3/fuSb7KCN/R8undl8i+NytLJmYhdRkBoRhFogHib27StwduHqW2S4Sj6/OONmFuNey6GF2uKUncXre1BxBRCyrt1JAAsF5x/JMbs3EHkaDjs8jUr/85Rm/tXTQrzVxQCzcNda0TsszrYSjpEgVytkiNR6CGw+nf56l+VM0Y2mPEYw8/ozM62ChFhQtvXShqYF8KPwdIzYxrTJiN9QXWmuhbijhmKn5qcOJ/Y+i+ZwaAX9wz0Xz/nQT7CTWtkygksu59A/OKnVMpS+WFeVkRJKRt9KgIQELTl6PrK5mHu5Eujfg5jxzf67sbuDW0xXbLvLkXGh2YHm8YP86yNCxK6FkCk/jxFzADG5SRx6zRAiww2YJMkUoHVZNBNBHoYcwhqWmKHKi20z4y2IH724PWLiuJJJqgOIJj4kEtpfBNqxqzqBCqmkUz/56Tu+1PEqREIUKNOsjVtKYiRvlv+u8j3k77g78FNSeEKWVltGLaUj11E9g5MReYmlOFmd1DOQaUFQmQX67Z5uYaBqOytNFoirL45bltUg/F5o283HbseqU9TyyfKDciJnZXmKehdODxHovcPClLfqggT5al4AqAIk6Tc2FcMzVGTX1FMtN1wZ++l8TYXR8WLN9JvviyqCWfep4gUrUO3xipFIK8q1KhTASx1PM+ayv54i9Cp4HYi0kxqC6mzprI5HHfxdydiyGUUSTmWiBHJRU4o0/DQnGLgGedYJO6idGSWUE4mPLmlKdMC5ImZHlaUPjc5VtcS4shHLgTSvkEIhzTBMst2jekvoNuw+UPmaIlxUZAAAgAElEQVT6/YaTw8jtbctm0zC0wt4ysXk0gzRDY3FwnNfycGzRbJ4B42RwKCZNUOJE44VyiZF2cXM5EUCZ/FzGUJabaIcS4xRG+2EgEKW1/QqgUZhMUBlhiWNhRh3qreD+Axv4H309Z92re01l89VmIlSMp1RMgHqzUa2aEcrPy7+iFoL2ggDU1auffFGbTZyT8l5feuuIbm/yHLoxjXvMk1MtJl3Gz/AhwM7GF/KAbWy4dxBZrpVN37D3eEs4yDT7iSdPhFcfbkmv9ZyfdJzuwzp0aK/Mmp4svWVt4NyX3Y9rN8GE0PCTR1odP4/aKTgA9KcNhn8vYGcBUwhO1Zfnn6zV6JDG+in1++qKAlAj8J1CtHMZCWoJHp4ilfGiJ53s3Z/9evbYa6ZuDJWz87GZDupdbRdeLkylxdZHcvBDP276lGcqJnRqXkZ9JVx4k06Y/my/5I10SrZe1WDqTk/5uCzFBKnrRUASSay8spFEL4Mt71rZXhN4kGArnOmMTSecXlJ2NgN63rF3nsh7yuxJS38jw1WFriFsp90ufcQmWPF0yQypnuGoRWqEoL5/BCiufVybeZs2W0qpHF39PXeyQo12IZjrQInL6oVPKJGiMBVIT1awlUxiqTDZoxLPYV+fWeiSTDQaYlqugFJ/SGvPb+cs+dPCSE+UkkHn8JwiNi5vijMEr11FGDcABR8p5NedmGHPywhlI6TcrohNZYyFQslF0JJXMxVJtKEeiRx8MLL9vG9KAfZMObqubNKW/VXDSVZWMbC8ATEGul2hjYmORD6aoX1Tw2AXNDehCn+NyjDi2yBGQzgt5Mdt6kDYJX+aQaPiRVcGxUkxAG6HskwGz3WxYr2XXOdG6ESQbP1T8LZvZb0CWM/iMRXrWV7PXoKYdDw9pTnL2FWdCXdkKsSUnI7Yvgib4QwpRdoDEAilvVhRN2VDRoGtoLYsNEy2rpiYSlRLU01w9mnNpYja8/CCDAw0oxmSAh0kQxjwyns0sd2PbOaZeBLZiKAxEG/B7L6i9wKLs8TpDMKpsOoCT68GWonEa1tSEHIWhEwIZVqkCjI5EBeUNxCCWjENWlOVKv6TEap4T5eqt0So1fguxiOMK+tTEs7dS8ZHPJe6FeGCgLtEZ83lnuyzrSzA/Nmgo/V41tfzpatTWkuIYATsgNLjmbHjSlrlVsiemZBL390x4OX9OkRAolqs08G2T32mDP6d4DaTy085Hm4y3GwFj5CEwumZx+iVYKZ0jR6pvCAOlhWftaoxk7YN/SAs9wZe/U5kfhw4/kHLWWrYXI+kK5nz04a9vkdlS3gKQ5fZ7ArDrKsVYY7h/PlkjOCXZ5CSxj55oHJbUofLlf97xnE9zaNW87UZq8pKwqcUN0ud2ysNeuraeVX/2Dm97r1YE9yG0qmgJIcqA14wPt3bP+v1XNVg5Q6qjR+Ph4xxWSyEkx0Ai2tEN62WSlPJWbu6LY3aA3vsw3P4lFS6UhbKc2I2KqbUcjAlIzlWZt/eWUjZioxL3pnaoch13JGju4GkiSYFHt+csTkUVi82fPxVZf9oYOc0k4dEvNuyfjGQr0I6aplfzvRLZRMTzaklfCZJdN6rxD98AmUpNcJM5M87lTo3ia2eeebBHSnBuzSZphlpoXGfqF6XxUgBsRSJUYitTLuUdpuFmq6vuoAP5XvFWRETxppH91k4Eqa/TC+MTfpM948EYjlpk82u8bxy0hCPAYpfbcR+5ZoShDoNG1Po6ptQ6Rf/DDe1bk4Ml2jZPi2mxO6++nQEyggnpaQ7xdHYiGlCFeXoxq7d9zawbC3b8fS8YTH06N5A+1iYP96yPoT1GSx6uLKJPLoqbHeUeOqUUC6Rm/IqcGDkHotJLPIXfdwljjo8faz+cj2UF6IyLmtFkMWTBPzXFNpc0pyKZQilkaXnLOrEugzizZEMElkZnV1MEZLPOnuO13O0lSjtRylFJGWhgpZhIUFAQ3GjpcZVxyzWCpOrgpSqHUsO/2TxbDkc57nLUbSdc1ASSE5O180bNzMXLOjuvRfzGLRy822ZY6JGI3gwPpBIYc3VOwPvv6I0n2xpHicrmL4Ey0ZYNXOW2w2nr7XEOz1BMrFVVn1AZ8J21qHnY1QkSHFIxPWTlvsdIzXOJ3lyrMELrUJaNqE8rnvvBfsGXylf12JSRUCzdVEVK6e0K3gH1YlQFsF2vWJdrMrcC/WsHE/yKNSUwvNkDj9H7JUS0M94O38o2WtejVTeW4d2FO/PM3x9IosTvqOS1wtfintvpZAEsBCf2kx7F7Ds86zGDTRFoIYzSn2EUt5Tam1DNDwZfONFjBMS6ykcEXKwRtX3rkKUzPZ6JC4S2w527m9Iktn/9oo+KjtrK8IebrUsVXl4FXigNOeCyFC84RJ1kFQEHnDzGaQOOHbHyH5sD6pVEKfok7pONQQoUveguqyFkglkYvCyGyfOXdypB97/biWiVpbYqDelHMlm/3tUSojuM6iRiGpqNLlH7VRFbqo6rxAPLenQVdcDRoAGwTweHYPbtmRj558J+qEG9ieqX9FxhpZ7c+UeYgXMdiF7rxavtQwSxlOXYuGwivYoBSxGcCuDBKJG2nsDO6uWkITtjYhEkIOW3fXAZjfQfT+x+UJDfwI3BS6h3DuIrHYa2Fj0xRyHiXZzodDxiXXiIFU0V7zS4IGqivPKYamQQWv9SM3QoWgnF2I1s2kraNh4hDej1vWKPzusUmBoBUNlV8zhEkoWcU7PKkrPg+mkLICDdkVp8DQiKZREk0cMV1+lJgDxHhrijhajfoQLXpuOouRm0YtVzKKaYE/DzQ58vcmgAexxQe2y9vdcigjsoBR7IoU2ccdC11y+/YSwt+alr8y58+6Szb1L3D9V8q2OV69nzjvlLPasZh3hvYGnL7W0D5TNC4nN3ox8HGnUtTHFcXDv3G66WMixSPrCyrt7c7GvXaWK8Hs17WP1SL4eniPo8ecR641hxnF/K4IWq4OrHGIwh2EcCIhhQBw5A5+FpjNcVDSNe8cyFNxVMAOTVJi60dNKSsdllNXLDEW4RqVlgm3J5qmGs2SyFVIASJYaiBh/Xs2QOwy+oELSDCES6n2OrkjEBZbiKBnmG2ZLnsaWJ/eVnaM1y7Rm/4MP2a5e5d7RfTY3X2G4kjino92Btsnshw3nDxfEpZhvGN3EB7JYXMTHHVgK00SzYHg4VK2nIz8pxSL4WrnGLvtSuchJUY1ZGIMWvkR2ZkMVWWtqORL01uPF3l8TdBnNe7VL6uT8s+M5eB5N50UquLaq+qVojHEZ/DRpEaZJJAcP2XhhjPfk8JikvdcDV8Vw6PSz6hKUcUFy4ZFtf0zoasGyAiSiD8atNAKFIzCCR7yDaLnOqt3hgzdeZDiFqy/scKY9qyEgrx+yvL/hWjjj6ekZLz64z/HjF9m7scu908jjgx1kqSwXlL7BFj7MPu8LLd2OtPBebhKLx1q5NnPaxp4nk8Ppz1tM97hGU8dK6jrh+8ZoqX5aN42RjFqxVwS2kj6uLZXCP9rvfSaTrSOZkUrNqPf5rX56geXyqbCNC57pZJCyBCMAYTyCZcPr//x3fKWLMJfMYff4MkKaxm6DNYXWEuxXSWhpFx3I5JCQHGpjwJGDdLWZUG3RMGfoZqxfDvxQZrz5xgGv//yMj7/dsbjS0X4w44u3LpH+ZOB4f8bmnTNkdx99oUdmHf3sEA2+6QISmQKC+uQTj5TJs4/9Qew5IlT+Thk1oaOZsZ/95MDXJXS6SQrMmfjPE/ytlIIn9cOqhWc1fs6xegZU+586BM/yenbKRErPtKylU7mWhYqMJ0vxHRSvxqondTS9dSmLu02JT2rBDOVR7X0B3Kv1baoGXDymm0pK0JjFasKmpS7MwHguZ9ubN/omWyinJHh6UJ6OTbvDardj2O3ILyqPP8h8SGBX4Vga5pcOWL3Qcv2lqwx7c+LdDf3ZmoPvnXLytVvo6VBMuUcGnJ6QCZQwwBEKFvV4q1djhbK23mvZ/jOJGA1omVmrdqiSC6n6z4rSklHbXeBHC+HvofFya5h37J0DcuVNayJClbZg3v8zvp4d05XPSGg5OWqSTgciY4XQf8DcUR56Ohxu+gY7uWP8tKzE5Fp2qoOvkzgSG82OX0uleHGu5XyDghQc49rXzXZJPtBoKWTl3GRRTg+ucfpGoHmYGZawPW04/d3M/HHm/BPYO92yfBo4OofT3HHptUucSoN82ME2WJjsidLKiBGpuS4TzexLUdO+p1bA7tHLPM1qaj3MLix+Me/6KdQIr6+CrZdJ++RgUmPq7t0C1emoKfUh18hbrYEVE/ELXUaf4fUcmk5GmkMZbbj48DTTQsa/h3KCbeH84exJfYoMFX5UHZaLEDBGH3zdfDTR1CkGnWS32j2luhn5QqaKaTf3DoUmW0sEC7IVEJ+VVEY7icA2gg6BJma6RUObIXwtsPrTQAoBhi3bj/fp4ik/f2ULc+XbfWT55gzRwPn6gCHO6dKqRFnGiIQJ2JicaauULx4iX7ei1U3QpuQS5dAal4haNNzWxX7XQ42Ooad5KVJOcazhtHzBM/aCpmk2ix+KHMxCRMffF5Mc/5OvZ09t0lJDUDCEF+36CBCc89KiakvLAkUh2Eb6lo8wTev1gqfnF9ww5nBB5YhKrlzNowMHizUL5cJ5E4glZptUkJCwUeKWsWz1Eli0hUyWplzSPndACX1AY0CSsnc88KBrmUWBtzLb7+8iLwty1vLje4HDRaJlS36lIbBhuBMtjUgiMEy8bPUTZitQIIbIf3jjgidOVkvhnqhjxVGjW9szk8TgqVuVAAa3ERWjFQtjm2g9mKUksII32bHpQ/Y+W/tQhHSkYJ5d6J6ZXFlnqRpBp/uN4MPOCAGiA16ti+ya0A2m0wGG9UJpORXqv427GolSnXyWIGXuQWlB6otYNeaI+QTHPzCNCUMJL5XewogSvK+b2RKEjvODy/SHkGMgHzT0O4rsB0LMxAF4uiW8nRlCy+JzmeFGQyayc0eZaWTYhRRafOjdhcaBxVrYsylOFVUDUnCwPYXh5kKO1Iq7oJ7FI8WRs5DX9DxGUTx7WRlsKSSWNSlqsiRmmMo1IUuSycFDnxTZlvE5xEj3mn71rILEcwidEc4TLFL+6VbT8YCHT1yrecNm9+k1BHII9TTWro5Vqxl280aeFnscMYMtqPcd0ckimANQBTpoXVPXXIYT/fekmnKKqRNyrbpSlLO9fUAYJLHdCTx8dcbuEKBbMGwEQqLttgzHyvZPE2sicjWQ5opqy/rSLtvZ3mRHfMEmx0J0Eon4j2zdBQ0+vqdW+UvJhK77Y+uVUdIYDsEI5iKE1WkptA1hUqkvePNtP7oucKabY9nX0i1Pn1mMgOeJvTpt4+asEESe1dEsOl785a/Q3bpmscKAPbzkkifmQmbnNoIVnrjGC+OZrkPQykn3ghEfBGzTW7wIx+4liE+lKd5gubYfEnNEco3VSvHsomZCGbYnIdc4Z0YYNMEW4qCgiWYJm6PEWgMyy/Q3W7Z7Hdt94dVXN7zabomfbEjXEtsDSwNaNTtF8qdjl8fT6trKsmpKcL4Cv/J+nXTtlAmexr5V+iEiarHtoA2BpqyLIpppigIoEA3J2LNXbsQtRlEUAhe7R9muhGpdyv6bkq0H6WcqdGN8bmTInRdqZx23vvw6uYvs33gF5kXbFa3tA85CjFbcK1IEruCDQp34v/1rRILl5/7oWoRJnG4wDNKIDfEoZcO1B0jEvu8JAFLwUSVhJ8XCUqY1Zolsd3fQICxOexbLYFgqJg6PT1k8Ac2BeALpcuSRdnyw6Yg3A/laR1xn0mxD7DGnxsV/IlP2FKP59y6iFG1eq+vEs7PHg38hQM8YiPerIlPOM5IkFkcqMoiMExBLyrnWq5jVmEaB7L6tWHzMGnJZ8ATZiQX8M17PLnTFDFhreS3mT6ELnMWOP/zuXX7r//4WP3ywYPflt5Bm6mFlaHeYX79G2J8RYkCCF6xwYcRjxYBMx05KzWb49Htco8nkQFRzWp/Qc85CFV7TkGVeQiiDfl2zkunjDo+v7RIFZsuB8CAzP8vMtw1DGGifBPI9pVutiCeZF5Zbrs0M5C/AGtT0Lcv9BR4qn+Kf0REqczjElZ8YRpNQN3s86FQxmDY3tBkQAp7g6ZnPRVNpaYLtNRJheqAL5KhWomq20Uuu8EcVL6EYG/KM2UE/c6Eb8ZY/eUN740Xe+M2/zAdPn/KN7/+Yj+/f5eu/96/48V1hOLjOeRKW28RZD8PlL/Pm3/3vufaVXyCEljqA158qmIn14WiGPybnt5pndz7ihZajFb/53VZTPmqFKrTlUPusVFxQy0RCQcja0peIy8lhpJMM28Tj3mpbEYU9oYk9mw8inxwtOXmktA8z/UfQt5AOGrbtwo7PtGa3HonJxuLIyX/ipk4cZBY2oPQNCU6yGG8X1AqP7JlMmAJaaSjP5qlCUh6hjo8qcGnMUCkUCs7tjXvvqD0XjalBSF51/Qyv52iKaCfAnG143Bzy5b/0d/jWH/w/nNw74kYHKYDwlNtvf4ufACfnZwxlsk784F/zp7dP+Xv/4Ffg0g8JR4lUBsHlIKNmHHItqxWzn/VVM0bcvDKamunJv7B5pVK/pvG4RsQXT6gPVbZREHqFmSibweiKR1cyh+2WTUqs1onTs8DspUBkh3w1sncnsn6UWC4SywMhd4kwU/oodcPxz/qU8Dkj4IWEfnBG0pYy+I1qHWpIi7Jc02aP44LVT6laU13PuyBKjaVr0WZaccB4nx5vVdxKjBnJzxN3hechh3H3HKSZ8+ZXfoHd3cAXf+nXOD5eMTu8xZd/9VfZ3d3hzjs/5JU3Xubxwzs8ffQI2jkhNnz44SPuHrdcfuvPc/yNP0S2g/Fr3QEv/+bfYHX8IUd//CfErVpGiG9AfXi9oP3UTylMtKbzf2PgLBSbZpVZhffSSF+gglVI+SfYL/VhDkMkbQLh6Jzt568iOqDXIT2Cdr6mvxUI//YYvXydB68e8nSv5eDDgXQE4RqkLrHuZpT2PBaSEyZedGnfWIhwH+XkGSBevCOFSMvu6esoXBZp8SygMRmDsm51DCmj4JnciyUdqGd/Zxc/ap6iKOpj5XGy3/ziLEqT41hf8lkE/AnGnaUEZ2dLHvzJt1l98/u8d/SAh48eou1tvveDuzTzXW7ceJm33voqq7hg74W3aNprhMUl/vxvXCWHFUFf5e0PPub2j95muVzT7R/y9HZkJ19mttMhaXOx71nV3OWbcvHbIiNOS+IbkIvpKuBYxxiokcWWnm65bd5LSokaUMmczucs2aCrFh169BiaNYT7Ee068koZFg271wO8JoS318hZy/ayEA6FcJ5IVzpW13bg/QZq06Gx6s3uvWTdYDrHvp3q4VLcGZg4Dq7tmGTguFbzb1Qs2FhyhkwFw/70PtJSBTuMP5Ma/R4D/0UYaxbPZC+ePQj2PEInSiTycLnizumSIT1hyPD4fG332W/YHJ1ySkCefsD/+T99k2W/ogmRTlpm3SWal36Fr/7Vv4LwLi++9XmGcM6j+49Z9z0/+Pb/xcsv/iq/+Et/iSe//3vIkMHJU2fDmcjfp+7NzMUY7K/mtQRrnaoR9X56Dp5d8IppKR5a6pU8E3Sd6BctYZkZFgnuWwhID4SwAVkr6ceCnm2J+xviE2V4ewlvtchGSO1O0Vaf1gTiDAVQ8ugqwpu+awIWJg9fC28moIGi4S0WP5pukJKQWSIZLmDiseBx1UStc+po6M2bRc2ET5N5XOuVLXjm17MH/IPWceov7LV0bcfx6YbLsxmRSBtM1X94mhl0zePjU5ZkZk1DUOFg+5TFkwWz4Re4dvPzfPEX34Jf/y/Z9InYdSTt+clPfsDi8AYf/PA9nnx0m9VmTUoDkuGFnZar88LMTSugZNyQioHw2IeOBKdg88zsacxh0IL1goupZdoKDfev7ZMWM/K1gBydEW4q/ETpXsyEk0T39Q9Y//LLcLaEuEQXHelLLcMKODSgHh8MpCc+n8sOkZmsMcUojh0hTBiCR2OkmjmvIjOowASfUn8+4tTRQHgamme3uEdsAuNDZy4eY2s9VvCb43il0jbV8tpt4Onvz8PTPbPQtSr0atoghwZmDenphkUMXF40BAmsBzjoBlYpM5vBIgu7rTBv7WFOz+/xzh/8L3w0v8V3v/4m/TCwWgck3GK+d43X/9wXuHPvIzYvvslHJ0vuf/IJacjI0LNKiauLvZLy5hqhGABX+2ECzN0RQczbE18XS9Y045qJxJJ9nMkSTROGlgevvkrue/q+hbRl+15LWA5sr0MTW8KXZwyv7SN37tFIIr0Uab6f0FuRsDa6Z7ufOLnasMmK6mBgX815amIsGCnTxmay+VOxMdxf2/y7Ua2RgxHPVs1W7fF4NanOhXMqo8Ya80qK9+ZpPFNULG5xKFV89e5GXnlyz3/W6znMa2C77VlvtkSJrNOAZqnEqujAelDOB6tUzyih6UgirNYbDnc6etbo+oz37hxx9/S7DL0S2wg5MJ/P+eThb/LX/tav8Uu/8J/z137jb5LyQDsXzk/O+be/9Y/g3l0zU9lrDgrtok2lD5wU9cjbOATEpM4RlKKEYMKXNRjARxlKmk77dEu40dP8ROhOkhHQ80BzZkU7ud2HB1vYmcHNDl1uiI/O2b66T7tO9PsNekPowwJtFzRpiVWbRQsZyhhHvbhno0bxVwqmbRpAss35mhJ3VU6m15j8TF0toTXEV/p0+qpVI23CWJPYqRycC+kEKnhqlSGFz0DThZDJDEgQloMy+9xXuRyU7dvfqbhik4XcNEQGRPY4me9yuT+DtDZvVAMf34O+79lDGdpMV/K1c15x8uHX+cY/e5dvXfsi27SBcMjuwSus1ye8vPMSjd62hStpNWOleyrZHMXUlIyXgtbGjZWAprHZv61XKkrGBugGhF6EvOlhMUM/J2wRwhuZ9G4gPRkYLjV05z1ho3TnG+R+Rnci7XxJPlW620esb96C9wO6P0djLO1dS1Fzifv6+E0VsYghDhm4QAWZExuKkFI32fm2UUmW76qFsuyhbSYG4tkpYi0WzCOgZiwWqmTEj1WF4RItYexdAs4eGA7MzxF+fY50dZBSCdMGkCdHrDdLunJ6cxakbTl88XOsnx4TZn+Vv//f/n1+9Nv/kKM/+gN2GuG8T8QAs055yoK/+Hf/Fu/+23/Dww8fMm8asm745O6PWN3+PutNYj6bo6psNXK0mPEbr+8VxpyiZd1Ry2RPVMbTecZQmh1Uw1UazJxaLw6LdwYVch5K3DOyafd48LlbyAqGL7Toe6Zm5CF0NxPptjD/4bfZXHqR+WZD/ORd+PnX0UVLutEglxvyAuYPz9GVMtQeJaUn3wRr+v1PU9PdhII1RrTvaH1eE4hUHJGJWS0CJ+Ubxv3ZghmzIdUUmtfsAmQTJuq4KEY8Z3/X2mBbxD9nTIuyGPZnIHQhCCklVJV+m1jf+YT9g8Arf+HznHzvfVQaQlI2n9yGYYXM3+Zb/+yPiA/v0w1bYtMQVVj2iVUvsBP48Xc/YP3oKZozOWXaZmAgE4eeGcoOPYRAyomdtrFYqp/sOOIRIbLZ9sxnnTn94g20R8fDz7QGyHms+M9ZK6DWrGgDm3aP7UGkO1oiRw3dMtAPESWzvSLIjQjHN1kdvs7yyR1Ed0gvBxa/e8J52kUWjX12q6TLwtlOx+GjAc93Q9TaRkwtUrVURcQctNd0/bLZk9+RiZqTgin8wFW6Q+J4cfXPVODiKE5P3/fP9Z4x45yKgpXdjtdQ2WdYDSZYD5Ks0DWQc2Y2mxO1RXMkqdAPPWndkxWGzTf5+A+/zeVZJqREnkc0wJCEWZM4fPMX+PJ/8Tf59j/5iM3Zmi5Y7lefe7bbzDopMSqbYWC+v8vuYl6zUowmqi4faOJ0G9mmxOU9U3lGfLoP68maliU7qPDOgxXtfMZODAyh5TQ1pKGnnQubds3u3SWDRg6/teLq3TOO7j+hm68IR5kwy2wPrqL7Df2lQ9oPhVXfEtqe0GQ4H8gPBobXOmQeGcKi1HrYQpY4SKVHcmlE0zj5K+Oqf8olmPwtVBPsfN1YLFh+U5zSsPSjcZwno0YsjXFMMCOQ6s/sAOdCoej42QUX2vmxeLxeOEH/6ddzORIe5D0bLF/sfPUa6ZvvsdcZHgjtgmbYwtDTMNDFhoiSyvTonJUUMtucefLeO/zg//2XbI/PyENPO2tJ24HcQxsAEpJ7YghWrNwuLFPENVbBJEmFx2cb3nmw4tWbh9wQayUdMDJzbPpD2QQlRriygLY1r3WtA4uQePds4KWdju1sxuZSg8oOy6+2nP9OQt9asP14jqYMOwE5S7DXINrR7A5wBN0ikK5ESANt6Al3A2FIyNDRtC0hu/MDPvsWpOQihhGj+ZKLnzDHeNSIhCVgFDHQ8f1TwfOQ2UVsZn+On2Ma3G7Fj4N5sT5I+tNXsACPjlnJUrp6PuPrmYWuEUVzZkhmtIYwI9CyzT1m4AJdNyduNly9MvDqoSD0vH8XzjZCyBbuGVIm9wPbzYecPv6ItgmkZAA1RmHTJy4tekIeON0GzrfK4c4A2xWEGbUdg1iLi8cnZ0iMzOcdt/Y7iLmAXeePqClB/meD8tLlHQYVksKCSJcjl9fKbtdwcuNzrF6+wu7752xlhhIYjoTm/sDsl6DfRvYffsB5G4ihpf3W91i+9UXW1xr0xUCQlmt/ruP0G0vyQWAtgmQhB60DQUZ6sQjeBNB70oNvcpiov8zYUwTAq8XURaNylbE4FC40Y+KDTs1mpaRdd+mo+KpXPAqptQkbqRcfBp2eI2Hp2TGdCE0MdE2gI7AazmlW36XtMpoDm7Th7HxDoGG7DXz4KLM3a9nmLYRsc97ywH6ElBpUNwwpEI3/gBJcT1k4XQYyDbNG2emERgJdUEJjzoCoCXAMyiAvVLoAACAASURBVAtXdxkyJDYcdFpqWctyiOOjUtekgmbTfIMqlImJEaEJkYNFSxtbdDbj0mrN/PyczfmM0EbCXofMM9sUGW5G9Mou+fpl1gdb9j68Tn7tgO6Tu8R//YDVzy0YspDnMFxq2O4fIqcQsuCUjnFcBRfVkNUoGPBpzObaOtT0KIUSMy7oVsfG/KM1GMNp1TGdGO2LEyg902KSOoWb4+k9+SEuTor6CIRnez07pguZPiVyTsQmop7FixR3PtMPA70qJ1tlmwSlZ2cWaNTbiwpDKhozNzztM3sBVBNkiERUe/qUWVnSLpvcs4/SRctRC+XBQ1Ht1kgns9tGr8A1ntCFCyGXDNesAUJCB1hvla4tW6QQ1JBL1kzcrJFhQESZPci0PewdD7S7wuJ+Im0DeXeX8OSpxWP3tuy+c8SlWeK0SdB2DL+zQm4K+UrH+XxOn0rOXslJa4LUNq/2/cJ5VjMqldTVieC555tw5Sd4cx2dvL9GNfAhy64LZZRKZPRiy+draZTkDoc7N+50fFoYbQWnCPVnKHSa7eZDVNqgzBpLkw4S6IvLM28bogZSv4VgGSEzMoRQ2nPBbDYnpTOCJPZFmTcNUlKPhmxlwlEii5CZB5hFazvaFkejFu5ALXv0NlVP1vDj4x5rcRoYKnCOrIbMk1WiaRsiEYmRRRwIQRhUGASO15mlZh4u9rnStuz0O3z+SuKHNw/IXxQu/X/C5sqSVYRu03Nw74Ttz11ic7ige+mQplfyvTlyq6NhSf/iDvPvrpk/7clNpMnJBCQaZzamdl6kS1zA/M/gAXkZUVkN8NdNt38Y+lDGXOIRZgQttbCVfS6VaCoXr6s6GlsZBe5Tf+AG1q79GWi6BgixUBBB0CF7CxKaEBhyps+KZrHi4iA0kpkHYRAjcLswp29hECVlISXom8heS6m9LOBElG3MtET6wU5bUyp1auGPe26l3iEQePfJin/5wQrvtgtUsJ0d56kUs9DSSkCicYwhtmSEGA/52l+8zvHDgb33E9/fKk2AzVPh4c2OjcLm8ZZO1zTdhr6/xN69Rzw42WX5pQVyFYZ5w4KGfkdoukySRJSIxETMkyhAcHqjMnbF1H5acxTUVcyZ9dqjCKF7r1ozjp1/uyigHvkQvBDJ61xVJvmIGtE6fJRCmyRqtFfcS87VsVS868PPWOhElO2QyBnWmwREw1dASglUmDWNhZRUuCzCjQXEPHBnHSAHckoo2aiBAF2r0NgYTolCVOXNlxtkfou9X/t1Pvqd32L5aF3YtlAHfbgJCsWEDCkzZGWdtVTh2oZZNZQyNuzz1B1FYuKlqws+frhmyBEdBkLTobHjxuku89dbTm4Jh+uB42ZNd9qzubVPf9bQv3XA9hsP0U3D7MNHLFLPrUtnvN5c4fgHdzm/tcfu8hHL5hWGa8Lq2i3S42AFNLhguDh52M43XwhirYEopLFtqm2v9UoOeNGNZf4azqvJl5RewGGS0Kn2/C4kY3qA/7gSdNRC9eJJBInFRI8hs5pEoRMn5mctdEgg54xmmLU9cR6QrbCIEIJVVoaXX6Y/fsJeL/z5v/AbfOGvfIX7/+Ifs/zRPXoCZ71wep7QpGxVaUJks820IbKjSqPKTpPY3b+Erg+IuSFl6CLMIjRiwl+1HCDFPC43yTSWhNIdtPQNqdioICMxEjhK5EobSFcWPD4fOO8bIoFZSOwcrTl/MPB03vD1X2zZe69j/o3vsPO168zvbti88gapu0x/XdDFlrBs2W6V5ekZcznj/Brsv3vC46PrpJ2G7f5VctMhw9YnGOPiURMmA9XYuqcwepdFw1WraMXPqFil/SQLxVTcOPvMEySUYpZcXUnRnGgRbsu5s6z6SGAo8WupvU6ql100rouxzdH9LDCdmMlq28Sbb+zy4FHifJtIasLYNh2Xdvc5P19yBeGlW3+FePmLLNf/nFUSdndadkXplsIQhTYpKWd22xkiY8O9d28n8p0fcPKtH3A4U0IOtDEyi3a6QnEigpjDEAPMYibEBkIqh7W6WjVu6QjKPWUR5UkfyWlgGDI5JQiBnXSDV3SHWyp8/FRZv7hg8UnLyd/aZ7Y6Zff3v8HOKx2hS+z/KLF6eY++2YObl5GX9ojLgXS547TZhz7T31hwdjgjxxnNsEZDX9FW3SaR0SsV5+pCPTSGYbW09vcJhcHaPhQhdJ9Sg/cwkcrnmXCPDJ7AmJZU8J+Pkk/lDbVTZ/Vkc5m4UyRZq2H99NP87IQuZMhBGHpIpzD0SohCE0ElcrgLMRxxe31CS+Dj3//fmP/JDu/e/ZDNFvZ2rWjZ8UGf7ZaXw0Anwu48gCYGDeRhy74IuyGSSMwYrD4z2kJFCSUkprQxotJwOgw8WSZCsNGajeOYsuhBxkkHNishctpnnp5vSQS6NhLCjCv7X+Dg1Y7vHS+5ex12nii72zWP9xYMUbj/9/46p1dmvPqH77HKR2x7Zfdky9Gdh/wwL2h2dmAIbA8b+v2GnXuJkzf32XxjgW6PC0HjDmS2VHUKWyYudKPAeU2vOKXCWBQdpBpnpmn7MnEWJhmEVehMPA1+ZNeg5dAHUcs0rtxdgipY5XNKA0yL0ZU8wc/CvG5LeGQelXvHK0IM7JSkwIDStsLDT54QFbL0vHd0G5rA8WZDzoHQRfrlkpCNU5qXyi9tzTuNIaKlXUNHZK9NvLAD/RBYRqFtIrFqA9ugGGxD9mfw+asNT3JGTweiT9gWOxDOWYXC1eUgNJ2yty+cnJWaUhKiA8N1+PoLDd+43nL00ZJLj1bsre+zyK8Q+i3tJpEP9omrJXJ9QXP7lHm7JHYr8vkh+09OOVopbWhJLy5ofnKONB39zg7xrFRvuJdZ8l0EiwvYOSktM4rmiI5fKSnn4qWCcsHrdHpFRKrD4UykluQGE8KxN50AjWusor2cKDbB9GZHRQDVSwAEJFVHxF6fhfdagOvLO4HLC+XW1yIff0d5sI1c3p/ztb/zy/yb3/omKfVcfyES3/wNztvM8Hv/itUqEdtQuR9CJicQFTbJvEMvyO4i7Enma5dn/NzPv8TZ7cd880lk3kFLwMplS+1mCMQgzJvI9d2WeNSbl1tiw5QFt07tpbhEIjkrjbTMujkxnCFiXF4ImZMrJ7yzd87icsvqaUOvA4v33+Hq8VOGr95kfueM8y9eYnVrQT6J5Evn9POO2VpJ9x4x34/s3D2BvYa4GWgpgL7pinksdfaaSwaJz2wInyo+L4SKODUU3OBaNwP1kP3oMDhvV2mXiu/K97IUM1vMrY7OgAmclNQr3/VJ4kH5fi30qZ/lztEzy9zzBPwTSuDORnmcWu58B/pVJofAapl5/3e+x/laoYHQzIlDwyz3SCOIRIsutJGTkIgaidEqn6JEmjKGKJSg+GEz40u/+g+49F/9fZp//D9y8J132eta2zQ/+SEUTBfoUVYaGTRaIxwBCQ0QIEvx/uw5QhAkBNoI+6E3cjlbhXoMwvbqjJNZy3kzQwKcvNnx4nsvcX5jn/2TNTf+8N9w/POXDf/dO2F19ZDNQYeerMmxQdsWDT3Noy2H93q6WUSPVsQsEMe+LqEkSo4F3qXml7G+NIZmQvKOwX1KUicSCXnUMIplaHvVnlbsZwubg2fhZXwYjAlVSVv3AAaO47QIpVLbwop7vy6MCpI+mxqJRbdFQma1VZZ9gqWwmEX2ZpE+Kx9/clrcfeH+gw17+/+etk3kbTLt0zYs11hlfx5omkAS2G63tF1LG62j0GLWcDiLyPEZ6Q/f4cH7x5xsMrdmoS58CIEYA02AEIXlZuD20yWnQ6KLoTo9ruVyeVBjFOw6TdOyu9gjhNOSri5I0zC8eBPmM14/OeeDYcnl06vM2x345VfY+yd/xPaVA9ZxzkDk4Mk9ut3EIQOruxv6z79GngXC4R7Sb7n8eEvbKut2ILUtrVoTQe9IYOG8UHDqKFjhAjHs880oLTlKyV9JeYql67Xa+aJx7JZdQOqDm3YqAq8lKVQu9PGzPgtGXKcLnKFqqoLnBYm1o6l6ZONnLHRX//avsPhHv8smFU6NSGwEglV1EoR5aCFnFgKL5TnSKPMgnHpOmCqzJrMdlHXKiCqLNjJvA00s1EDKnG4zf/r932b+zu/zcLXhvOlgFmCYmJ8g5sg0kWuHkZ3DOe/1J8TzxEwEzbDJgd791rqwhnk2KfHDO49pYmsLJmLJB5f3uNmv2IQ1u2ng6vKcYXZOs1Fi27D6yz8P257r//5t8nVBNwPdZsl8+YDN+ZzDsM9d3RLpaaRnZyts2sjmxi24/yOs5rYA/uBtYaVq8dFJMDgSKwlufFkoOVJjy9gC8UMhmbNZkFQGlfg821yEL2JZ1xkjzEUFDbmkQI2F1nYXLZp7E/Kq6dxT9Va2E+flZy107cu/Tpz/McP5QBC4NBdeeGmX4+MNkjKHrTD0ieutcnURCOsjbp8lZosd8uU5uTRw6YKwIZRsCiXp2PpZREht5FyFdT/Q5BVbDRAC86YlDAJRkSYQopLblrzokJsv0L/0ZWL/XS4/+A6/cvMSZ+s133w8MIhwPhifZ2nbEQmBrErfGz8VSj+RbgZXdcsbfeYHc6W595iTGzMuvXOHF197id3LDcM2smjnDF+6RX78hPknT9Crc67tdSwfnNHNOrrzNbuNsj1oaDcNcm1OfH+BxkiTLJkzlh57QaU6Cy50sRwqozqM5WmkUD3YIJN4YbvNqVBM4LRQLlkt6XXaBpaimVCK8EOtGhYB13KMQiYEa4hZMPmY/MlYZ/tZmNd+/5fZzF6kaz6iiQs2r36N87duoj/+FuH+fXJoubyAN157mcVf/irp4+9y/rvvstQGiQ2xCfSS2NnJtKmBsKFbHPLqr/4aT+/eZvPxx6SUiE1LDsJmsyH3VnQcs5llaQa6nQX52lVyl+CLvww3fo7FpZfZa1r2v3WHz+2/wF//r/8bPvjjf83HX/930Aofnw4MBBugW+ikoEKQMu0n25DdS7sL5nsdTw7nvHTvjJtfuMZ3f/KQg+0TdEi8Ejveffd9Hs8XXNGBfqWky/v0b9wkvnuGPjqhb69x4+GS03bgetoj78OltdL2W0tCDZEofsiM5K0eqgiNzaOaeLhKE8LYt0XEMHFxvKzCyz1Ncwqyepiw4EYotcMl0wbwxtTmvEoVHi18nuRCBONli2P4zl65Jnuadow86+uZhe6P/o8/4vzJMa1GQvcl3vzb/wNf+8V9vvVP/mfOH/42eZ04eO0Vul//75Bf/Ap5/b8i4T2WZ0v6Zc/i4DJdUHZ2Wk5OBzQEWFzl9S//Z9wdfp9PPrnNdp3I24GkFvlomoYQsnVAAuaNMrtxk5PPfQUZBp7egYN+4OijO/TbnqcPVjSi3P/tb/HBo3sc58gLcyWeR0QC89JXpGsCp9vAoELOVmsQQuRgf5evScNJmzjsA/cOF3z+wV12X3mVHw8Cx5Gwu8Nid0bfB3aObnOkHc1LLfOcWLx2i+HWFeZnCT05Z2fnKk03R45PaXd6YhAa2rKZmSiBGKaZMzL28ivp7FHKDNYQICdS3hCb/er1ugNQQ3/ZuDRn/xKNjTq1FBpzqMRqGnLJhVOT2tKDGKsHdlLaayGIJqE6Du7ECRQR9LMI+B9//x8xrNcW9E8PuP0vvsHyxy+w/PGHdJpZzGeEL/5d9Etfpn/7bfo//gEnKbDqBySYNzTQcvQ4EaKh3rR8xB/80/+dWXqKSE8KGbSnBdY5sBoS8xhpQmJxE3Z/7iVOv/8u3/4XTzhbDWz7ns3wWzRxn/3dQ44ePSENA//qvd9jS4JG2N1pCE8TIpG9LqABFrMZ66drUomFxphpY+S1Gzf5/Hyfo9tbQupJb2cOh0ucL3b5ysdL8vvf45O/8Hnkj9+j2Zux0J7L8x3aD+9zcPaAvaNTrlzf496VayzaA14+fcDu+cDd/Rnbm1eQbk7c9HhnqOiCVpyIGOyLampjoUyKeIky6Nbwr3px5fhSVUIEzaXmo7R/IJiO8qoIw7gBQrbYtGoheTOBFs+AcZNtln3UjKFyeXIhmvszF7oreY3MMotXbwGZ9d1/yPEnkVnYgEYWMSIP3uP9f/rvWNz55zx69BOebJSkkZBhe7okqDDQkEIivPIFdq42HH//J8ybnp1Zx+nWBDQnI6IjRouEKOTNioc//IhHHzzm4e0jznvh/pOnzObKtcu32JnN6FHWKqw30GtERJnFli4GJASW2fDJZgMaWyt/bIU2ZQ7bll/5wkt87lA5SvD2vSe8sXuF7a2O+0+X/Opy4O29Ddt0hc3dH3GtfcpOM/Dwzvt01w+5dTjn4PASH35wlxubjG4jB6/v8YNbL7E42hBmCdqWkAakIDLn4Dy0F6szEcqXe7LmsZMVUiREK3KakmOaLYXMBCOWgH15h1jWssj/396bB9t1Xeedv7X3Ge70ZjzgYRIAApxAEhQn0aIoyZKtSJZiyUPHcbtSiZQodqfTrlQmV9qtpJPY5XZSTtKZ4ziJbMmRbdmqSLQtSwpNyho4iaMAgiDm8c3Tnc+wh/5jnwdCbA4gBdJRCqvq1nv33HP33uecdfdee61vfStkozkf6MbEuyoCETLBvBfEVrZbNXG5Kk0B4CI/it+IXGzMqlXY7EornY5KbptssK6g3c/wDNEuVB2MFFjnWW8/zOrZr+EZkpWOgRHaeUmrkSCNlNJlYapu3MS7PvaPGG/1+Pwv/B2GWZth1KR28wGi4QX6x0+hlUOR06wJsc8o7fU8fv8jnF+1LBlwJlQstDZhya5Qb2zBo7DiMR6sCFpiEh1RjwPq2YmEQkkqCkVKqnzQWDvG0jqbdlxH3NqMWh2ijGekNUnWF1RWkhSeSV1nYm6J4crzTO0YQyeOzU0H06NoBavzy0gywu6zSxzdfxv9WDG9sISenqaWZURRQqz6G165i3x8UfX/RZ+YCuXKjbPUoySQSCJ4iYiilEjFVU7CC0ugVyFas5FCEyBcleJQBbVcdcleQsUgHF4MG24RBfiNmfKSlEVPmBU3dsLaV2hsVYHIqvauuNKt2gh6hrK/TiyObYlG1TxjNcHFGgrPem+IsQW1WkHhgdzTqgV3iK7sFOM9FKs8/rknUEmXsj+kFgtMvZM/8/GfZuXEMzzyr/8+Sg2IRrcweudt+BOnSKd24EnxuoMtgzcpUcLY+CbqjRZWheiGiMIpIRSe86hYM5Ik7GkEmq4zJqYvmqgi6Am+Mg9RRGk6rJ47xtHTiySNPvPn6yz32vTsefpDodGKWVh9hq21PjVbY3p6kt26T1yztAtYSwQ7vYXNesjxdofB7i3Ybo+RKGZmuEIiHq0jIq8qN4nGK33RnusM+pxfnGdt0EGJUE8T7r3+QNhcIBR58Hkq70l0dEnYy1+0y14I+gneqYBxrD6zipAvjKBU8MlRhQDxYG2wO50KGGzvTPDp+QAts5US+w27URTiKkSgegOW1/FahzyPyIyidDHLicY6GC8U3htEFKM1z0jTUYtjUgyTgG016HZzKCyx0uBKsMt0D/9LUI5ESpyKUe1TfO2TTyDtpxmLoF/WmLzzr/Pen3ovD/znf86Zx7/KYJgxeeDdTLSa+IPfZKQxhapvpqg1iaIa0m/jRaN0WJ5igVYSc80ovPftB/Cmyx8+tYT1msj5kADrPdaUuFoMo3VWe116+TL7djZ59tCTbN+xifVezHPJOtt2bqY8ucqgH9FbX+GOqRZ3fugDLJ9d4PjCImvzK4x6xdSebeyNEjb3De3hEKdzWhMRtVpE3SisjxHCTHZuYR6LJXM58ytLHDp7kk63g/eeA7uvI9ERB3bvo1FLMNqRRgmx9sQqqqIBVfiKjVzfkPwTEZipBhVINWxrQ7J5qOsWjumLTt4S47NgzekIcT4o30bEwqvgYPcgylalr7gYIbo0t+OKKd27P7QWMF/OM8gj+m1h0FWsrArdTkq/m7Lci8hthDeORGkK40lzRyJCf6XHzoagrGCVYcI7loBapFjNLev5cbJn/yljDGlow4SC9hPf4L8v5bTWj3HdD6a47g1MfvDn2TRZ8uiZ80yMNugVHlcTOrlHSYxEGp0moARlLRNN2BTtYPyWHyJbOc/YqT9m6DTWK2xZYsqSIlekzQa61YLC0xlmROkUk2ad67Zu54kTQ9569zvYMV0n6/U5+vwJlC2o1Rrkp2K6c5apyQYTrkPPj9BShvraBaZdQa3ZYD1NcDrlppkWxdkes2Wgyzc4Ti6c5OjceTrDAUWek5sSvCczhsdPPMdSb5Vjc+cRL+zaOs17b76NRjTGYnuFR449RxrH3HvjrTTjFLzDYDEW3rKnTdKsc/50zmqvhXcB6KCcsNpdQcUpI0mzKhDMRR5opasZmCoxvHr5ilyIDSiUl4sz3KWsoFdU6T732W20Ys9IHVqJJa6VjNQ9u8YMzWsHRPUuKnYUhaUohbwfs7YitNdr9HrQzxKGJibLFa1ayqLyKCWM1xQKw66mpZWWdDuKTq7Z7Cxm+HXMsSeg5jj7zTq69Jz87S/wqM9JFy/QX3UMbcRCLiyaiNJGlM3N3P7jf46YPhcef4pGErHsDfMPP0677KOaE0y3GoiOGPSGdNfbeBkwMjFJUh9hbFPKjrdsp5GkXHfgVsb3387Yw5/i1FKH0dtvZu3I89Q7K0zvvYa8mXLf4/fBzDT37tjNu5v7OdV2yOQoU+uz1JKSWFmmZ1do7drC/g99gJX7H2bt4BF6aHSU0GqNsry2wqAYVo9NKKwNewZrWOv2OLe4yPT4GLdfu4+1dpehVnzt8BM8deYoDsX5tWVu3r6Ht127P4QEV1aROGHfjpT6aEQ0iLGAKR3GG46cPcbOHbuZqI1jnCA6AmfQKkErENEV63oYj/dctA/x6hL+O4W4DQLvN8BlMl/WqIkgvQAOdNVWWylPIo66dtQjRzPyjDZLxluWkcaQmX0Faewg6YOGwir6Q8VwDda6CevrEW2bsrYeE6uIXEVYp+hKjiSKuoJGpIgHBf2izdb8NznXduxISxSK2bLE5Y4y03TLhCg6wDW73s7K7GlWT32OYvN2ymvuZuG63cw++gTNyQl2Xr+PWqvJ+VPnOHXiDNZ6xiYmaDQaWBsyOI1SeF1jeHSRweoFBp0Oh5bPky+eIJ4/RDoRYaObmc1K9m++npmdexm9+51sd57VjiVqnGb+9Fm27mjxVGqIn32W2/QEj33zGe4/8igXuj2G3jO/usiwGOKdrRC61XLloSgtS+uraDzLbccDTz/E2MgIK+2Cftah3e8xKEtOL8zy4HNPctPhp8hLw+z6IltGN/Hx9/wYFkXhC6x19MuMlfYynXJAr9/jqZVnKUzGDW/ZRzNO0SqqoiEbcVlBqky9jcqX3gerUXsdkseVQ5xHvRGx18mphK2bhKzwlKWnKKA0FmNDEbS2h7ZXmMITZR61EgxT7xy+sDRTqGnHeB3GGjnjo5bdMwX1vQNU2kESIc9gdT2m0xYG7TpzaxG9vMZalnChZ1CqzlQCjXrJZDOmzDx1LLePehYj4Ztth+09zX3/9N/jy1lUNk8xmGQ834Waa8D5c2zbPcPubVuIx1o0zxwhzc9wyjomW9dTZDniBTvIoJ/z/Lkupv047bXztI8fZXxylFFApSXSWUDHGtFCefZpuvvqjKWT2IU21g0YLLWJdcSOZpNzzx9jcP40X+p3uO/rn2Mu65O7UDO3LEtGGg16g37YeQrsnNnC1Og4J8+eozvMqEUxe7dvZ2FpgfVBjzMrHcSbgLauQlVZv+Cx498OG1rn6Ha7/MbXfp9Ws8mpubOUrgDAOkte5HxDaYqyYLTR4qHDD7Fj8zYaUcr+3dcyEteYHG8Fn6HbiLWG6IYHxAa3SxQL4jRaOSjfgOV189YWe/Yk6DgGKkypjTAenGhKY8mKIcWgoN8pGA487b7Q7Vlyryi8wljHmY5C2jWY88TP62qWtIymMF4vGW8aRkcNO/YabtZDotEOZcfSKRy9YUR7NSFuJ5zpxbQHgpYU74QohQ9ss1hpU699mdWFElqG1vA8yw/8An3VYCw6T1c+gB/UcdYxeP4QM4t/TLPv6T4+z6GF8yStlDNf+wrDLZMsb76RWMV0Mo/SBba/wNa4oBtnDPoLmAvHWbtwkjMLQ266cRO9555nSJPxG/YxHKyztrLGI19dprVpkkLFnG63WSqzyiELkfLs2f0Wut0u3hkGRYHSEUlUZ2ZyhpXlNW7auYN+XrLS7bPSH7Ip0VhTVni8KiZaETRvEHkLkJkhz5w+SBwlWGswzlabACq/XPARrnQzVljhzPIZBOHrzz3GSNrguu07+P5b7mTz6BjdvMPkdEl3tcbR+Vk6wz7Hz5/mlpkR6s0aI1O7McXlK51cLh37Q5/+KS/OI1JDRCM6xpdDUDVUEpPEATxkVajI0m5nzJ95nu6ywdqYfOA4cGOTNPb0c0M/8/T70Bs6sgJKExKxheAEjUSIrSERw1gjZiIasGksZ3y8ZHyTQcUZznqGOfR7wuJKQreT4rIEZRNmB5rxJGayoUm8pcRRayhMeg2tmfdjpETNfZaR8iz9ToaRlJP16zi3ss768iwHpuqU8RTb3/V+Dj70MNHaBaamp5C1I5xZ6jKbxazoKdZ7feqRZXJkJ/v37aQ2vpnxHXt5/rnDqERYunCaWmMciTSHTh1lYe7MxYhDHCWMjozRHQyw3tHpdEjSGnlhaaV1+vmAzSNjrA36dLMhOMf4xCjzi+2AULmkwMkGbAk2YF0vsG2+gB7ZwBOGY0opokijtVDmwW3iXGBT16K4Zss23rJphnMrs3z/gRkee77L0bnzGG8w3iLWUU80qDpOJRw7+uxlad5lK521x72nh9itWAc6quH8Kvga3tcIKYnBd6OUpjQ5y2e+yKlHvkKxsEI3U1x30y6iRFCRQkeQ25LBcMORaSnKgm4nY205Z71tWF2zOBOcvEhBURh8AQmeZp1r9QAAHT5JREFUVCyj2jFVy5loWaYmCiYmDfWJAp0YBh3N+qkRVpVh2Ncst5t0BzHLZYzENYYqYcSv0TAr1CXHKcXKpmkOr6zSLy071wtuThXHyibnYsOgk9EqI6Ybfea0sDjvaRuFNzARhWhAo16jHidkXpFnBRMxbEtLlnLNcmlZGmT4qlaax2EtGOexDpx1KG/RcUxmDNpF7GwJd8xM8DtHljAURBJRq9fo9IYhUqNCDohSQhQF1gAQSuswJniEbIhuQVXDcgO6rvFI8kJAayOfGO8prcPawPakKtp/rTTGmmDLK9CBwhRESNMaeT7kwoXFK6t0fqOccfgaL/i5v+MsvK3CIsrh7CKD9dP0zj2JMYI1Q5wr8LpJkqbYsktW5FgbkaYNtNYUaPq5Ye7EM6yemce6mCJL+YH3bKW30uXsbM7yuqU7CJRiRW7IswLlHA2lGY9zGihiBty+LUVabeq1Eonh/EHFE0s1ltDM5inzfUese1x/04AdMzHteo3l7RGza126f5Cxq+tZBoYH6iS1JvOPLzHdNfRE6IxaJI0ZnvZE1qKUUAKRish9SWyFhBBY9x4yCTm4KoLRUcXUOKwsW5Y6DmfDfdPKIamCEYUZONKBp64157slPoJEKbwOCmJ9CF9FSahf22pppkY9iGfZe/Khplj3FL3gQgEbllYX7DEljoktE+TDIcUgp5ZETDQ1trQsdy2Fc3greAVJFAU/oKv4AXHUIk2khdLCSC1hqT1kdv7ylO7y817hEl9MhTqolNB7wBb49Tn8hTO42rXo3dNIMsPIpq20pu4K0JiNstsbKAZcWAZchR+r4DvOO/bfepiz3/pPWJMwvnUfY1NjFJ2c6WsNvcyiahFKF5T9Np3ZRYadHu32kOWlPqfnc3Q5xo6ozqkzNZY7BUeWBownFrRhbujpe0WnLFClZ76/g7/44/8XtcmYx/qf4yv3/zGz1vPtNQeRwjxWsOOWccpMUaIxDmKBt/349Tzx+WP0T4CvWfQ0xCMw4iOyI45uZqsIQJgdlPboWHjnu+/lr3z8L/P5z36eX/vUf8OaEuMFjCKd8ux/z05O/8kF5tcKvM03wB3YxDO+s0FSCuuLGdKKqe9UmFWILfy1v/a/sm//zfyLJ/8jT3/hNLV9muFBYdhxRDoKtl3pcaVDtKYRpRQ2CwRszvMTP/YObrvlbn7hVz7FsbPnsQJprNnyw03swOMGlmLOk7YMm+w4f+knP8LvfOYhFuZOE78hZZoqFbuUwsp7KLM2nUOPYRaepeidoRi2ie0WksP3oEc2ozZNMbp7O2pUB3+OxLyQ+RHyJ0SBc5b+egfTM4xsnyBt7WXvPT8DvkCSBt5Z0pE+TecYd0LcGEF0DZMNyHedJh+CQbNw5hkmDz3NhVNDbDJOPjmOq2VErLJS9ohUTn8I7aKksAZvHeePxXz211aZvNaRX6tYPejozwu2AG0sksHZR+YoyoI5rzDOo5KI0eZ1oGbBt0kSx4GPXEfabPDwfzlInkNZ2o18KhIVEoJKGxHteBdsvptedhTvvkDpFMYaPIJajTj+5WW6bYvynrKKx4JCac01t02xdGaImc1ozni+7yffi1kb8Nh/eZLB+t0k6Z2MN+8nHl/ih372o/zhL36R4aPHsViSZkLZHgBh+ZybXwyzl1JYPDp9G6P1H2Us/RZwAScWYyDpwB0f/BHGt9zCM7/3Wc49+xx777yGG/Z8lM1Ny4I/95pAnJe9vBZ966PGJSUqq5jf/MlH+fZv/SI9m5G4nKz0TEQJ5/oppSSktSbTU3cytfcerrtrL+NbpoIf6CJ3hmV5bo2jzx2kM/cgM5NNrr3rf6M+1bqYcXRpkbRLV/gNdKuv+Ey8g5X5E3zp07/CqWPn2TLW4omTbayF3DrqqUOlOeurOd3S0x1abOFDWCmKUEpAG/r9IfnQ4p0L4EkP9Ri6Q49Xllg8kSjSkSadboZYE5wJ9YRaM6FY7dHQQmY8xgUzxDiLE0HrhLfc9tNs2nsbR7/8j2m3z2CMZVjakCuhQ/A/N+aFTCwCTg48rZE6ZWHIs5KkVueeH30Px4+cZu3ISVqtTcxsneLC+hkyO8VHf+W/8siDj/PEf/4/iWSAB/IilBTwOJTSF72+kYaxxlbG0iZL/TkGZhDo3XzYbGy/4yOU7GLl4Cdxtsv01Cg7R3cwv3yaRuJY7GQcOjF3ZW26hz71DX/nj99F3AhZVhtKd/TbD3D/J/8J/dIyWsuJjeBqnk4vJtURU62UiVad0tUZ23YH9/z5j6EbI1WrnnzY5nc//W8Y9p8mLyzXXTfGdOPdbNt/PUltM83RnSRpCoB1Bttdx+SCbzRpNFO893TbXYr2gCLrcPrsAxw6/CXW1jwTjc089+hxGmIpnaKRRFiXE9uCvlPMd0pwJRGWsVpEzUMcRZxbH1A4h9KeztAyNEKshH7pmKlBQyvGYkVmYTUr6IvQzQKPitZCM7JMtVLOrpZYXzERCBTWM6o9I7U6uU4xRQ9jLJHAWh6S0GOtsN5TWE9WWhAhiUJUYGg8sQTbzniHE0UtTlHeIGJoJkKqFf0clEqZ2Ho3jW0zHP7Wl2j4PoWx9HMDOKyHKIrDTIcn0Rt2WugHCRUs2XBY6ySAN10J3lLTsHVijIVuQSOGrLQ8duTUFbbpst/Hu2vxfppLY7uiDK1tKU2EKG4EX5G2NNHUdI3piSZUPEqj42tABoxc/L7DsW1sgdJpTrU9pw4ZeiMPsTj/PLPLimtvuIutU9Ns2X0NThuO/8mX6Q8NatNuJjZtRyeer371GdqLHQad58mz01hyBkNgqk+Za1LRjEd9bJYTScRILJzLYVeSUADGKFoEarFh6Zlq1TGmRAmk4ugZqIlnuh4eRqd0dHIh1UJcSxmXGloZArebo6aFPNc0ah5vQ03pSAkuDoo1tJ6IgjSqEelA8jihIHceJRHWOrRy1KMSEY2WoCT1KEaLqfQgxTmHYFAqRmuNqIjCauJIKFEszZ8gnztE7CJyN4kBokSB9Ij8JM4vU5MM8Dh1LQZNYRdwqgCXorXD0cTQALYg9vHA36ciBk44vZphHLQzQavkslXpspXullumUckLhMcBnqzYc+1Wdm3/AJKMQtLAOwGxKDWJyAxhh2vBd4AaWo9+R7tpqrj3B24gX5zipgtdOmsZ7Qy6g4xa7S6i8Vt46JFvs/3kE8xdGHD2XB9XzmHUw+Br0Lidu9//Q0y9t8Zv//vPMVw6h/clhbGc6Kbc+OGf54HzEZsf/rdElNjoANONh1lRnrw/QeZ6IbY4nAAEqzrgcoryGqxvEskTINDxCYoY6x3NkQ5lkdAZ1in9AKVSxI3i7VF0VKfnNc4otBpiJQUZCahdv4xXEZodeNfD2yWQsYDtI8ZLhnV1UDHiwfglkBTtejjl8Woc51YRIkRPgIqxrONcD5xH0UKkgfMRngjsTiwXcLKCiibBBZKg0p5B1CjeDXB+EGqh+W0YU2BlIdiW3oO3WN/FkeBtVCXnBGSocRaUYK3DoPC+vPJKN3dhnd0H+hCvB6XDIIwzPHcazh4hnk6wjQTXMfgRRTy9hyhqI3ELV8Y4v4YgSGMHSkYIgWSNtznDtS7PH17kSw8sc/OeGuOtOtmwRnPsVsbqO3DuDMeOPsj6ygr5MMbaHqUtMCbF9yPyzmZ6An7YIjfgLZRWUYtvZ2bkHewYlvTKvRR8C2e+yeq6o8gKMptROoP3Cuf6WO8QKXEWrFvGyhrWdsMOmxASsgIq05R2SGbWKJ0NaYVotBj6WQmicNYQRYL3fZzvEGZ7A6Jx/ijOW/Al+G7IQfWBtaC0wYWykYgT6lvYwFKgejhjEAQnbYQYj0WUxRqLyIANmHp4HQM8zls8cyGrwgcmVGSl4pXZKCn3YICnexeUq8IDh2e9ivfHcc4QKY9WRcjZ0K7KI1MXW7miSvf7Xz/Lz9z9KNJKUCrH+RRJbuOPvnaC+SdPcuO+Gg8+leGskCQRKj7NrTdPsPP6Bl/84jrKO0YaDf7SzxpqSZXv2djBuTn4d//kScrBgJGxgiPzY4yNWYZZTrb0BZ45totB/zmy3jxZN6M0BcYG1k5jhlj1VX7v0xqc0Ol9haxYxbugOKr7JJ/5dw+SFyXGHQG7gmCRNhfjls5VGZ8VlMJXdAvWd8IDqTB3G3sYB6wNLrkxfgO2HTZX1gVHt/cOKYJbaQNkKSIXMW/ARc7hDXJBRKr+NhBFG6tK1cZFNxMVOGBYwd6rytkbhUfcxnc9VITXzpuLUYuNYJmrqC0C0UVJHHmmxmIGA09nsPH9MC4f6fDDIvj58Iap8YhB11c0GW8Acnh+eY72wa8i4omcR+mI+r4jqOJZ0mRALc4ZHe2QRGGnl+iI8WaPyKRMNebRKmJstInrWcrIEYnGuUnynibRizSaQk1HrHd6FEUSEqXLb2OKw5TFgLzMKIqcsnSUBqwnsHlygWHxOZwVynINUxZYXykLxxi6f4VgMW4N7yx4h3c2gBU3wj4V8nujWqGv0vSs9xfRNP6i0r3wOAMpz0Y+bWCDmoiE2DusDsZ/IQFWFBS5xOGIlDBad8Ta0+6F66By9joFXkLO6gZ/3AupzFWSudJhlfCgtUciizIRb70+4Y53bePcyVUe/OaAIY56MkLWz3AmJFQ776udcLCnN0L5VjQfvrPF3/rFe/n6fz3ML//WeQoXdtMTm2qITvClYPIC6xwNLfwfHxzn8NEeX3q+CPfgSitdXWXk610cGZGvGJN6MGiv4nPL6gpsioQ0dYyO1InilJqK8R3LjgmFaIdWQ84eXCYvPYmHaLSLS2vs3mrJM4dSjqYrkdiRtkYZDJtQeyuD9kHy4Vn6y57cKgygGjXGp2fwKiau3Uxa30LZfhJ6z3LqyCy2bzHKk0z8WcrVmJb/DQalYaUUiBUSxxS5xRUlXgzbRmK27ojYvFlR9DwnjjU5mrfIXYHxHhGNJUJF04jKUG6dZisiLS3TTtjWSDmY5/RqdQb9FsN4O/3pW3DNrURlydja80ysP042OM/NexQ/94lbiPOSX/r7T/Dt2RBHFmfQ2uG14BwYY8NMpioWUhFqDUUtSSgyhzihMRYR1z35iuF/edcM7/vf7+HQHxzm5LHTNK+dwuWa2efmyAsbdrzuBXCc9+FHJNVsp/I68bM98sWU8dYYxoWwZmQEJArOWR0TS3AznT4FqmyQaii9fSX1eX1K9yPviJntLlfhGo/XmvFzMffcmqL2KBo1jdYt6tvHife8F0nuQqSF+Ce4uTiIyi3W19Bjt4Jsq0JlgFNs3nEfw0GbortGrEeZuuaHaW66NUz/0TjOZrjhN1h76Mv02p6k1WLk+rcytud9eGLEJhAJwocYnv4Mx3/3y5TDgrzfAn03edQiHTnFkZPP8sxcn7F917DrrXdy6rkjnHv6aVrS5mf/4nVc88Gd4BXmeMFT92/lvt5NPJxNccE0KXDY3FZweEdzeIyfuH6BO2d6bI8iust1TpzezslkjM5whjLZAhMjuGZEMjCMnd3MaDJkpKvZfwPsufZG1o6cwaBJY4Wt6sx6HDrWeO+JtEI5IY6ii8nXSRSR6pgoDuz2aRwRSUSUGKTQ+Hab7pxBS0orapIVnlpUIxaH9Q7rqtmuWqalejmB2XV45P5Znj1foHRVbsE7rFVgq42CdzgvZNbxyJkMAFvBtK640t3w3rtJtr8V0RMvJNfqLZT9g3SPfYuyhKwYktZ3E7fuQfQ+oAR2Qb2OeFPxcNyKsKXqOsf7HjM3fBj8HPhzwPUg3xfQq74fli3VwLsam5oNJguNliZudQgjR/FlAuvbUdvGoHWetKa48QcPoDoGf6pDb+0wvYktNGa2kbbW8I02g627mZjaSW96wGD0JLu0Yuv4FiK9FZJJSGD/vdvZNrmP968n/INnxjnma1jrKUvAQWz3MLb5ON93S5falGPxKUervZWB203eSiljTdQQVORRRlHWa6ylU4wUW7BFH9PJUP2CyVqDC7GlxOCreLZSAYkraJTWaB29QJgjQoQQqQiDDZsJp4i1Qzvw/YyyCHm+WmlqiSKOEzShHIMRd9GudPiQ6+BCNtlILeKmO+usIjy34nA62KEq1uiK5rcsylD4WQlvvy4iVRF/dFCwb8RMV993D/A2vExtmKGAJxnbytSddwNdYAHxm/Gyo0oVWQcW8cwSwuEaYQr8CEgCrCGyDoQdGrRAxkOcFmGj8IYgeFVHpSP4GEpJ0S7HyRIiCu82gTMg64hSRI0pnBugkiGN8SWat1rUvhoHbt7FTU8tcGylyaHVVfJuj6l6nbs2NWk1xxA1gTebiFojjDZ2Mrp5C2Pac8f2mDPrQuYUagBJ6dlfF94+ldB0NdxKjioTtqclTePIlMIkCqcdMVATx/6xAR8eaTMxzNk0Zon6A8pOTlIVb9GKi8nRusp9DVQRG7UyNggVgxXmlaC8QhMFQhwfoi6CYLGoSFEfaZJ3cmLRGB02Sdqp6vxQPgERdJX8PdGKmNw7yTUXuqSHCooyYPUirdERREmM8g7jFV4ZJuKYrfWEVlLS36ifcCWVzksXWAPS6kgGlJXyjQS/EAakVW2228C38cVBkD7OGigSaE6BHiWY4gvgungzxNMPme/RMkiXwI3RBiZwoqHsY3slxTCijB1xZomWOtiew144SZqvEk2cwnXaoYJk5sFZdFSAKsMPxXt06XH9Ln0v9AYDTFGyPtCYZUd8ocRLiaw0yNujqKWYJeVYKxUTsWM2E7xoECh0nblsB/vbfbTuEovixlF4Z5Hzx3k9cPMaIfKeae9552jKj45NUTeOtf6Q7Ihh6YRhKE3SSCjF4JwB74gqaltXFTVRVRL1hu1FxVWCgLJh5xhpTd6NGD47QDoxqRL6K/1gq0WBuyUKma4BkeQUGk9ZbVS0KIoCTj7Q4+xsjuADZs5rxIZoi3UGb0O/sYb1PiTGkSih90YkW2NWEL0GMkUgKOgBBrlIVjAEcjxdhG5QGHsWBkugPD4rcGsevWsJUVvC7MU6zreBHEqDN4I027iog/g6SK/yRTlcb4GiW7LUE0xUkhZ9xiOh7BmGazCl2/h8CfptaI3gS4vrldiyR9wZwfUEWR7QO9/muV4TMzGJsQXdwYDzXtG/0GZ07wBp5bjM0htY6jXHtjHFT454TiwCZTArSoR5qzjXjyhHhEg5VOm4oSm8b+8iB5YMv7ywmYESLAGGVI812tWhUJihY+FcyfyqZWhUSCOsjKtQ92uDUIeAT0QugiSqtOuLXjTnQi20WAkrqwUrz6/SXi+IBdYXO6ACqnuj7kcIZ1SUXx605yIfyaA0nJ4fMLdaMSxsUOe6UOLPWIuz1Qxp4NSy4YJ29GxFFH6llS6EeHoIq0CM+C5eBkHx/FiYzVhB/GaQIjg+3QDfzZAYMBYZqUM0DmzCU+LcHBRFoCXNHFUNY5Q3QBFsHJ8BFj9co7PUo8cojohSCrLZNTrrGcOVAjOYZ2t/He1zpFR4bbD9jGEOOhtHugq72MUPM2plTnd1jWGvi7Ge9jBj2O0wPjuE8RKflVhV8GzbsdrXfGEIs0WlQBUePDeK2V7K0voY0yoG8dw5GQCZu6M6k9rTc6FQSyFCu1Cs2xoj/ZjzbY9PHN2hoTQl3pZou1EVcSPKX3V0qbJtJDR7XXEEe6xxWIFYKfJCWFw1DHIXdqvWYiMP1uMqJHFI932hkgRVZpf3jtxGnO84umVgvi+tuUijiwqlO8Vu1JJwzGJJlNDPTYgTX2mlIx+CDkoGZVhu/aDK9o7xfg18F9RY+Mscvujg+zlkFjrhptryHHpzhMQaKVbxnXXsusd0S1QSE8+s4KdPolQKxEACtodZbmOGGUrX6VphsJpTDAesr/ZpKNjdrCGUgQ41K/G+wLqwC1SdDnQjKAoyI6z0BrQ1eGsqWowYZzQu86gsBac50a3z2UHMMyKcU0KvCL8IsSH3VxnoaM3JLCVWmrW+Yo9YooalGGoa1lPbYFXNY2aLMVbqLWo2piElZ1cNs50I49PKf5cHfcIRETYSnsBYZSXUalAXnf6GjZLsYdMhDLCcWi/Q3nGqa1jJDF574o3EaReSajac4O7SAiUKtBO6hWV54OgaW/HaXUKL4z0RgaHBeY/1lk7mSaII41xF2nN5ctkok6tyVa6UXL71d1WuyhWSq0p3Vd50uap0V+VNl6tKd1XedHnDlE5EdouIF5F7LznmReQvvFF9vhHyvTjml5PXcy0iclpEPnElx3HZSiciMyKSici8iMSvs7+twO9d0qYRkY++zrbeLPlTG7OIfEJETl/BJr/jWq6UiMj9IvLrl3v+a5np/jLwh8AK8JHXOC4AvPfz3vvs9Xz3T0u+F8f8cvI/zLVcRKW+wougnKeAHwZ+DvjKZXxnNyFSc+8lxzzwF6r/T7OBhWTDb/mybX0V+E8vOvYJ4PQl738duB/4W8AFYAB8Dtj0KuNsAf8vcA7Iq3H9/OWOmZBl1AV+6iWu3wHf/zL9CvBrwAlCDPEk8EtAWn3+0Zfo6x++TFvfX33+PuBr1bUfBt7/ovMuXkv1fg/wFUIg/Szw1198r6tr/sfAvwRWgQXgVwB9yX1/8Thf8povtnmZSvcBYJEQwdgKFMA136XSTRPCG38DmAFmroDSdYD7gFuqB3EMuO8V2pWq7ZPAjwDXAO8C/uprGTPwq8CDL2r7F4CjVA74l/kh/yJwd3WvPgzMAf+o+rwO/DLhxzBTvVqvonTPVM/qWuBTwDow/jLXIsDTwKPA24C3Al8koCxerHRrwN+r2v3z1T34WPX5GEHRf+eScSZXQun+G/AvLnn/ReCXvhulq94b4KOX0f/lKl0PGLvk2J+p+rz2Zdr9gerzO1+h71cdM3D7pf0QIDLngL97Off3knb+JnDs5a7xFb63oXQ/dsmxmerY+1/qWgizogf2XfL5JGGWfLHS3fei/r4E/NYl7+8Hfv1yr/NVbToR2Qr8WeA3Ljn868DHROQ10VK8CXLYe9++5P03q783vsz5dwBr3vvHv5tOvfdPAo8DH68O/RCwhe+8Z/8/EZG/KiKPisiCiPSA/wfY9V0M5elLxjRPoGra8jLn7geWvffHL/nOKvD8K7VbyYVXaPdV5XI2En+FsKw+Xu3cDPAZwi/pw6+349coVej8O+T17qBfLFcq+PwfgI9WO/uPA5/33i++3Mki8ueAf0tYlj4I3Eawnb6b6ype4tgrPePLvfYXt+tfpd1XlFf8ogSY6scJBu5bX/T6TeCnX2/HlRRcXiWzRWDbi47d/hLn3Sgil2Zz31P9fe5l2n0CmBSROy9jDBvycmP+baAG/AzwIcIm4ZXkXcBT3vt/7r1/wock1d2X2deVkMPAtIjs2zggIhPAda+jrdc0zlfT1g8AbwF+1Xt/6NIX8EngfSKy+3UMckNOAe8RkW0isukVzrsf+EER+QkR2Scifw9450uc54FPicjNIvIuwkzyh9UDfSl5APg68Dsi8hER2SMi7xCRj7/M+S87Zu99n/BD/GeEneD9r9AGhGXslqrfvSLyN4Afe4m+ZkTk7SKySUQar9Lma5H7CRuPT4nIXSJyK/Bpgs36Wmf/U8Ad1XVsejU/7qsp3c8Aj3rvz77EZ38CLPGCHfN65G8T7KpTVVsvJ79BUKB/Q7CddgL/6iXOewz4BvDfgS8DzwIfe7lGfbCCP0TYGP0HgiL8JvBKP4BXGvN/BBKCIf5qD+5XCQ/5k8BThF3sP3zROZ8HfpfgH10iuKuuiFTj+1GgT/jh/QHwR4R78Fp9ef8MWCYo8RLwjlc6+X8aPF3lEd/hvf/BP8UxfJCgKG+pDPnvKRGREeA88Anv/b9+o/r5H233+T0p1bL3FuAfAJ/5XlE4EfkwYTl9DtgM/N+EpfWzb2S/V1EmV0Z+DjhE2GVfsSXwTZAGIbrwLGF5VQS/6sIb2en/NMvrVfnekasz3VV50+Wq0l2VN12uKt1VedPlqtJdlTddrirdVXnT5arSXZU3Xf4/ZVkckKDGRusAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "endpoint = \"https://computer-vision-xyr.cognitiveservices.azure.com/\"\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "subscription_key = \"affceb640b2c4f66b9297157fb9cbf1c\"\n",
    "\n",
    "# base url\n",
    "analyze_url = endpoint+ \"vision/v2.1/analyze\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"http://img-arch.pconline.com.cn/images/upload/upc/tx/photoblog/1108/17/c13/8684555_8684555_1313596511781_mthumb.jpg\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "# 参数\n",
    "params = {'visualFeatures': 'Categories,Description,Color'}\n",
    "# 请求主体body\n",
    "data = {'url': image_url}\n",
    "response = requests.post(analyze_url, headers=headers,\n",
    "                         params=params, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# The 'analysis' object contains various fields that describe the image. The most\n",
    "# relevant caption for the image is obtained from the 'description' property.\n",
    "analysis = response.json()\n",
    "print(json.dumps(response.json()))\n",
    "image_caption = analysis[\"description\"][\"captions\"][0][\"text\"].capitalize()\n",
    "\n",
    "# Display the image and overlay it with the caption.\n",
    "image = Image.open(BytesIO(requests.get(image_url).content))\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "_ = plt.title(image_caption, size=\"x-large\", y=-0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析本地图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'categories': [{'name': 'building_', 'score': 0.6484375, 'detail': {'landmarks': []}}, {'name': 'outdoor_', 'score': 0.0234375, 'detail': {'landmarks': []}}], 'color': {'dominantColorForeground': 'Blue', 'dominantColorBackground': 'Blue', 'dominantColors': ['Blue', 'Black'], 'accentColor': '0E4B91', 'isBwImg': False, 'isBWImg': False}, 'description': {'tags': ['water', 'outdoor', 'river', 'building', 'clock', 'city', 'large', 'body', 'bridge', 'view', 'boat', 'tower', 'lake', 'tall', 'green', 'colorful', 'train', 'flying', 'blue'], 'captions': [{'text': 'a large body of water with a city in the background', 'confidence': 0.885956381969993}]}, 'requestId': '3c3079ad-4f89-4c64-89b9-195a1c9a46d5', 'metadata': {'height': 274, 'width': 182, 'format': 'Jpeg'}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "\n",
    "# analyze_url = endpoint + \"vision/v2.1/analyze\"\n",
    "\n",
    "# Set image_path to the local path of an image that you want to analyze.\n",
    "image_path = \"OIP.jpg\"\n",
    "# Read the image into a byte array\n",
    "image_data = open(image_path,\"rb\").read()\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"affceb640b2c4f66b9297157fb9cbf1c\",\n",
    "           'Content-Type': 'application/octet-stream'}\n",
    "params = {'visualFeatures': 'Categories,Description,Color'}\n",
    "response = requests.post(\n",
    "    analyze_url, headers=headers, params=params, data=image_data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# The 'analysis' object contains various fields that describe the image. The most\n",
    "# relevant caption for the image is obtained from the 'description' property.\n",
    "analysis = response.json()\n",
    "print(analysis)\n",
    "image_caption = analysis[\"description\"][\"captions\"][0][\"text\"].capitalize()\n",
    "\n",
    "# Display the image and overlay it with the caption.\n",
    "image = Image.open(BytesIO(image_data))\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "_ = plt.title(image_caption, size=\"x-large\", y=-0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成缩略图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thumbnail is 100-by-100\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "\n",
    "thumbnail_url = \"https://api-computervvsion-cyl.cognitiveservices.azure.com/\" + \"vision/v2.1/generateThumbnail\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"https://img.zcool.cn/community/01e8635c7cda32a801203d22e1528b.jpg@1280w_1l_2o_100sh.jpg\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"affceb640b2c4f66b9297157fb9cbf1c\"}\n",
    "params = {'width': '100', 'height': '100', 'smartCropping': 'true'}\n",
    "data = {'url': image_url}\n",
    "response = requests.post(thumbnail_url, headers=headers,\n",
    "                         params=params, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "thumbnail = Image.open(BytesIO(response.content))\n",
    "\n",
    "# Display the thumbnail.\n",
    "plt.imshow(thumbnail)\n",
    "plt.axis(\"off\")\n",
    "\n",
    "# Verify the thumbnail size.\n",
    "print(\"Thumbnail is {0}-by-{1}\".format(*thumbnail.size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取印刷体文本和手写文本 \n",
    "* 假如我们抓取了1000张网页，出了文本信息我们分析以外，还有每个页面的图片的信息，我们可以用提取图片文本的方式，将图片的信息也抓取下来\n",
    "* 我们进抓取了图片，想知道这些图片的内容是什么，也可以用提取文本的方式进行提取\n",
    "* ...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"status\": \"succeeded\",\n",
      "    \"createdDateTime\": \"2020-10-24T13:45:25Z\",\n",
      "    \"lastUpdatedDateTime\": \"2020-10-24T13:45:25Z\",\n",
      "    \"analyzeResult\": {\n",
      "        \"version\": \"3.0.0\",\n",
      "        \"readResults\": [\n",
      "            {\n",
      "                \"page\": 1,\n",
      "                \"angle\": -3.6952,\n",
      "                \"width\": 474,\n",
      "                \"height\": 472,\n",
      "                \"unit\": \"pixel\",\n",
      "                \"lines\": [\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            253,\n",
      "                            218,\n",
      "                            269,\n",
      "                            217,\n",
      "                            269,\n",
      "                            228,\n",
      "                            254,\n",
      "                            229\n",
      "                        ],\n",
      "                        \"text\": \"P\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    253,\n",
      "                                    218,\n",
      "                                    264,\n",
      "                                    217,\n",
      "                                    265,\n",
      "                                    228,\n",
      "                                    254,\n",
      "                                    229\n",
      "                                ],\n",
      "                                \"text\": \"P\",\n",
      "                                \"confidence\": 0.955\n",
      "                            }\n",
      "                        ]\n",
      "                    }\n",
      "                ]\n",
      "            }\n",
      "        ]\n",
      "    }\n",
      "}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json\n",
    "import os\n",
    "import sys\n",
    "import requests\n",
    "import time\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Polygon\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "text_recognition_url = endpoint + \"/vision/v3.0/read/analyze\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to recognize.\n",
    "image_url = \"https://tse4-mm.cn.bing.net/th/id/OIP.hdPk_uIfrkC_-pyURhblEQHaHY?pid=Api&rs=1\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"affceb640b2c4f66b9297157fb9cbf1c\"}\n",
    "data = {'url': image_url}\n",
    "response = requests.post(text_recognition_url, headers=headers, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# Extracting text requires two API calls: One call to submit the\n",
    "# image for processing, the other to retrieve the text found in the image.\n",
    "\n",
    "# Holds the URI used to retrieve the recognized text.\n",
    "operation_url = response.headers[\"Operation-Location\"]\n",
    "\n",
    "# The recognized text isn't immediately available, so poll to wait for completion.\n",
    "analysis = {}\n",
    "poll = True\n",
    "while (poll):\n",
    "    response_final = requests.get(\n",
    "        response.headers[\"Operation-Location\"], headers=headers)\n",
    "    analysis = response_final.json()\n",
    "    \n",
    "    print(json.dumps(analysis, indent=4))\n",
    "\n",
    "    time.sleep(1)\n",
    "    if (\"analyzeResult\" in analysis):\n",
    "        poll = False\n",
    "    if (\"status\" in analysis and analysis['status'] == 'failed'):\n",
    "        poll = False\n",
    "\n",
    "polygons = []\n",
    "if (\"analyzeResult\" in analysis):\n",
    "    # Extract the recognized text, with bounding boxes.\n",
    "    polygons = [(line[\"boundingBox\"], line[\"text\"])\n",
    "                for line in analysis[\"analyzeResult\"][\"readResults\"][0][\"lines\"]]\n",
    "\n",
    "# Display the image and overlay it with the extracted text.\n",
    "image = Image.open(BytesIO(requests.get(image_url).content))\n",
    "ax = plt.imshow(image)\n",
    "for polygon in polygons:\n",
    "    vertices = [(polygon[0][i], polygon[0][i+1])\n",
    "                for i in range(0, len(polygon[0]), 2)]\n",
    "    text = polygon[1]\n",
    "    patch = Polygon(vertices, closed=True, fill=False, linewidth=2, color='y')\n",
    "    ax.axes.add_patch(patch)\n",
    "    plt.text(vertices[0][0], vertices[0][1], text, fontsize=20, va=\"top\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 学习人脸识别和计算机视觉心得体会\n",
    "\n",
    "> 历程艰辛！    \n",
    "> 不会放弃！    \n",
    "> 终得成果！    \n",
    "> 分享经验吧～"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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